WO1995014966A1 - Automated method and system for the segmentation of medical images - Google Patents
Automated method and system for the segmentation of medical images Download PDFInfo
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- WO1995014966A1 WO1995014966A1 PCT/US1994/013281 US9413281W WO9514966A1 WO 1995014966 A1 WO1995014966 A1 WO 1995014966A1 US 9413281 W US9413281 W US 9413281W WO 9514966 A1 WO9514966 A1 WO 9514966A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/155—Segmentation; Edge detection involving morphological operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
- G06T2207/20041—Distance transform
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30068—Mammography; Breast
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
Definitions
- the present invention was made in part with U.S. Government support under NIH grant/contract CA48985, Army grant/contract DAMD 17-93-J-3201, and American Cancer Society grant/contract FRA-390.
- the U.S. Government has certain rights in the invention.
- the invention relates generally to a method and system for the computerized automatic segmentation of medical images. Specific applications are given for breast mammograms including the extraction of the skin line as well as correction for non-uniform exposure conditions, for hand radiographs, and for chest radiographs. Techniques include novel developments and implementations including noise filtering, local gray value range determination, modified global histogram analysis, region growing and determination of object contour.
- mammography is currently the best method for the detection of breast cancer, between 10-30% of women who have breast cancer and undergo mammography have negative mammograms. In approximately two-thirds of these false- negative mammograms, the radiologist failed to detect the cancer that was evident retrospectively. The missed detections may be due to the subtle nature of the radiographic findings (i.e., low conspicuity of the lesion), poor image quality, eye fatigue or oversight by the radiologists. In addition, it has been suggested that double reading (by two radiologists) may increase sensitivity. It is apparent that the efficiency and effectiveness of screening procedures could be increased by using a computer system, as a "second opinion or second reading", to aid the radiologist by indicating locations of suspicious abnormalities in mammograms. In addition, mammography is becoming a high volume x-ray procedure routinely interpreted by radiologists.
- a suspicious region is detected by a radiologist, he or she must then visually extract various radiographic characteristics. Using these features, the radiologist then decides if the abnormality is likely to be malignant or benign, and what course of action should be recommended (i.e., return to screening, return for follow-up or return for biopsy) .
- Many patients are referred for surgical biopsy on the basis of a radiographically detected mass lesion or cluster of microcalcifications. Although general rules for the differentiation between benign and malignant breast lesions exist, considerable misclassification of lesions occurs with current radiographic techniques. On average, only 10-20% of masses referred for surgical breast biopsy are actually malignant.
- an object of this invention is to provide an automated method and system for segmenting medical images .
- Another object of this invention is to provide an automated method and system for the determination of skin line in medical images.
- Another object of this invention is to provide an automated method and system for improving the display of medical images, such as mammograms.
- FIG. 1 is a schematic diagram illustrating the automated method for segmentation of medical images according to the invention
- FIG. 2 is a schematic diagram illustrating the gray value range operator
- FIGS. 3A and 3B are graphs illustrating the modified global histogram analysis
- FIG. 4 is a schematic diagram illustrating a partially segmented breast image at this stage of the method
- FIG. 5 is a schematic diagram illustrating determination of the object contour
- FIGS. 6A and 6B are schematics illustrating a distance map image and the subsequent threshold image
- FIG. 7 is a schematic diagram illustrating a segmented breast in a digital mammogram
- FIG. 8 is a graph illustrating the performance of the segmentation method, evaluated on 740 mammograms. The ratings were subjectively assigned by 3 observers;
- FIG. 9 is a schematic diagram illustrating how the segmentation method could be incorporated within a computer- aided diagnosis scheme for mammography
- FIG. 10 is a schematic diagram illustrating various uses of the segmenter when breast contour determination is necessary.
- FIG. 11 is a schematic diagram illustrating a segmented hand in a digital bone radiograph
- FIG. 12 is a schematic diagram illustrating a segmented chest in a digital chest radiograph
- FIG. 13 is a schematic diagram of threshold of an image of the hand
- FIGS. 14A-14D are plots of the pixel distribution of ROIs of FIG. 13;
- FIG. 15 is a schematic block diagram illustrating a system for implementing the automated method for segmentation of medical images according to the invention.
- FIG. 16 is a schematic diagram of the method for the automated detection of skin thickening
- FIG. 17 is a schematic diagram showing the method for the local optimization of the external skinline, in which the contour of the breast is straightened;
- FIGS. 18A and 18B are diagrams illustrating a ridge- seeking algorithm
- FIG. 19 is a graph showing the gray value profile of a breast perpendicular to the outside breast border
- FIG. 20 is a schematic diagram showing the output from the skin thickening method
- FIG. 21 is a schematic block diagram illustrating a system for implementing the automated method for the automated detection of skin thickening
- FIG. 22 is a schematic diagram illustrating the method for the improved display of digital images
- FIG. 23 is a graph showing the average gray values along a distance from the skinline before and after enhancement . Also shown is the fitted enhancement curve;
- FIG. 24 is a schematic block diagram illustrating a system for implementing the automated method for the improved display of digital images
- FIG. 1 a schematic diagram of the automated method for the segmentation of breast images is shown.
- the method includes an initial acquisition of a radiograph of the breast and digitization (step 10) .
- Noise filtering is applied to the digital image (step 20) followed by application of the gray-value range operator (step 30) .
- Using information from the local range operator a modified global histogram analysis is performed (step 40) .
- Region growing is performed on the threshold image using connectivity (counting pixels) in step 50, followed by a morphological erosion operation (step 60) .
- the distance map of the image is determined (step 70) and the boundary of the segmented object in the image is then tracked to yield its contour (step 80) .
- the contour can then be output onto the digital image or passed to other computer algorithms (step 90) .
- noise filtering using a square median filter 3 by 3 pixels in size, is employed in order to eliminate digitizer line artifacts and spike noise.
- the advantage of using a median filter is that the noise reduction process does not affect the smooth edges.
- Figure 2 shows a schematic diagram illustrating the application of the gray-value range operator.
- a 7 pixel by 7 pixel ring kernel is used to find the local maximum and local minimum pixel values.
- the difference between the local maximum and the center pixel value, and that between the center pixel value and the local minimum are calculated as the range and stored for later reference. Pixels yielding a small local gray-value range are considered as possible "non-object" (non-breast) pixels.
- the range is determined on the basis of the base width of a pixel histogram, as shown in Fig. 3.
- the global gray-value histogram of the image is determined as illustrated in Figs. 3A and 3B.
- the original histogram (Fig. 3A) contains gray values from all pixels in the image.
- the modified histogram (Fig. 3B) contains only contributions from pixels with a small local range (maximum minimum value) , which correspond to possible non-breast pixels.
- the criteria used in classifying a pixel as a non-breast pixel include (1) having a pixel value close to a global histogram peak, (2) having a small local gray value range and (3) being part of a large connected region. This can be thought as obtaining three sequential images.
- Figs. 4A-4C illustrate the effect of these three classification criteria, respectively.
- the direct exposure region (with black corresponding to a gray level of zero) would have pixel values in which the local minimum must be small and the non exposed region (with white being at 1023) would have pixel values in which the local maximum must be small .
- the image is in the form of a 3-gray- level image, where one value corresponds to potential breast pixel and the other two values correspond to potential non- breast pixel (either a no exposure region or a direct exposure region) .
- Figs. 4C illustrates the partially segmented breast image at this stage of the method.
- the darker pixels correspond to non-breast pixels. It is noted that the image may contain pixels identified as possible breast pixels in the direct exposure region and in the border.
- the three-value image is subjected to a morphological erosion operation using a 3 pixel by 3 pixel square kernel .
- Such processing is necessary in order to prevent the "breast region” from including artifacts from the film digitization process, which may have gray values similar to pixels inside the breast region.
- the filtered binary image is then subjected to a contouring routine as illustrated in Figure 5. Note, however, (by comparing Figures 4 and 5) that rules based on knowledge of the mammographic image need to be included in the contouring in order to identify and eliminate the "transition zone" between the direct and non-exposed regions (which is included in "breast region” in Figure 4) .
- the image becomes a four-value (2-bit) image. This is done as follows.
- the rules include analysis of connection points, corresponding to points with a concave angle and a short connected path to the outside, which are used in cutting across the transition zone.
- a distance map of the image is calculated as illustrated in Figs. 6A and 6B.
- Figure 6A illustrates the distance map image
- Figure 6B illustrates the subsequent threshold image obtained by thresholding Figure 6A.
- darker pixels are closer to the film edge.
- the shortest connecting path of "breast object pixels" to the outside i.e., film edge
- the thresholding yields possible transition points which are then analyzed for presence of "sharp" concave angles. Then, the contouring routine need only track the pixels having the single gray value corresponding to the breast region.
- Figure 7 shows an example of a final segmented breast in a digital mammogram, showing an allowed connection at point A. At point B, the connection was not made since the concave angle was not sufficiently sharp. The degree of sharpness required to make the connection is empirically derived.
- Figure 8 is a graph illustrating the performance of the segmentation method, evaluated on 740 mammograms. The ratings were subjectively assigned by 3 observers. Note that 96% were considered acceptable for use as input to further computerized mammographic analysis methods. In the rating scale (x-axis) of Fig. 8, (1) corresponds to optimal, (2) to minor deviations, (3) to acceptable for CAD purposes, (4) to substantial deviations, and (5) to complete failure of segmentation.
- the segmentation method could be employed in an iterative manner as illustrated in Figure 9.
- various parameters of the method could be iteratively tried in order to segment the breast in images obtained from various film digitizers or direct digital devices.
- Figure 10 shows examples of how the computer-determined breast contour (found from breast segmentation) could be further used in such methods as mass detection, microcalcification detection, and skin analysis in computer- aided diagnosis schemes, and image enhancement.
- segmentation can be used in other medical imaging applications including segmentation of the hand in bone radiographs as showed in Figure 11, and segmentation of the chest in images of the thorax as shown in Figure 12.
- both global and local thresholding can be used. Local thresholding is used to segment bone from skin.
- a number of ROIs R0I1-R0I5, in this example
- the corresponding pixel distributions for R0I1-R0I3 are shown in Figs. 14A-14C.
- the pixel distribution shows a single peak with no valley (Fig. 14A) .
- the center pixel of R0I1 is set to a constant K 1 .
- a valley is found at gray level p 2 .
- R0I2 If the center pixel in R0I2 has a gray value less than p 2 , then the center pixel is assigned a gray value of K 2 . If the center pixel in R0I2 has a gray value greater than p 2 , then the center pixel is assigned a gray value of K 3 . In R0I3, a valley is found at gray level p 3 . The center pixel of R0I3 is assigned gray value K 2 or K 3 if its gray value is less than or greater than p 3 , respectively. It should be noted that R0I4 and R0I5 will have a single peak distribution similar to Fig. 14A as R0I4 is entirely within the bone and R0I5 is entirely within the skin.
- the advantage of the local thresholding is that the peak shown in R0I3 may be too small to be detected on a histogram of an entire image, as shown in Fig. 14D.
- FIG 15 is a more detailed schematic block diagram illustrating a system for implementing the method of the invention for automated segmentation of medical images.
- radiographic images of an object are obtained from an image acquisition device 150 which could be an x-ray exposure device and a laser digitizer, and input to the system.
- Each breast image is digitized and put into memory 151.
- the image data is first passed through a noise filtering circuit 152 and a local gray-value range circuit 153 in order to determine the initial potential regions of breast and non-breast.
- the data is then passed to the modified global histogram analysis circuit 154 and the region growing circuit 155 in order to determine a partial segmentation.
- Image data are passed to the morphological erosion circuit 156, the distance map circuit 157, and the initial contouring circuit 158 which determines the contour by evaluating the thresholded image data after the distance map is obtained, in order to determine the features for input to the contour connection circuit 159.
- the data are retained in image memory 160.
- the superimposing circuit 161 the results are either superimposed onto breast images, stored in file format, or shown with all non-breast regions set to a constant gray value. The results are then displayed on the display system 163 after passing through a digital-to-analog converter 162.
- the segmented breast image can then be used as input to a method for the automated detection of skin detection and skin thickening as shown in Figure 16.
- the digital image is segmented (step 164) .
- a gradient image of the breast is created using, for example, a 3 pixel by 3 pixel Sobel operator (step 165) .
- local optimization of external skinline is performed (step 166) .
- the potential internal skinline points are identified as a local gradient minimum within a certain distance from the outside breast contour (step 167) .
- An optimal track along the internal skinline points is found using an energy function based on connectivity and distance from the outside breast contour. This energy function is empirically derived.
- FIG. 17 illustrates the local optimization of the external skinline, in which the contour of the breast is straightened. Since the segmentation matrix containing the skinline is subsampled, inaccuracies in segmentation relative to the subsampling factor occur.
- the gradient image is calculated (step 170) and the skinline is determined (step 171)
- the second derivative of a dark side LaPlacian is calculated (step 172) .
- the ridge of the second derivative local maximum is found using a ridge seeking algorithm (step 173) . This yields an improved skinline without the inaccuracies from subsampling (step 174) .
- Figs. 18A and 18B An example of the ridge-seeking algorithm is shown in Figs. 18A and 18B. These two figures show gray scale values of pixels of a portion of the image.
- the ridge-seeking algorithm produces a gray scale skeleton (four-point connected line of local maxima) . As can be seen from Fig. 18B, the maxima "ridge" has been extracted from Fig. 18A, thereby improving the skinline.
- Figure 19 is a graph showing the gray-value profile of a breast perpendicular to the outside breast border.
- the internal skin contour is identified as a local gradient minimum (as seen in Figure 19) .
- Skin thickness in this example measures approximately 3 mm.
- the output from the skin detection method is schematically demonstrated in Figure 20, in which the light gray colored region corresponds to the skin.
- the nipple has been indicated as well as a skin thickening.
- two expert mammographers marked the external and internal skin borders in five mammograms with skin thickening ranging between 4 mm and 2.2 cm. The distance between each point marked by the radiologists and the computer was calculated. Good correlation was found between the computer results and the points marked by the radiologists. The mean distance between the markings by the radiologists and the computer was less than 1 mm in all cases.
- FIG. 21 is a more detailed schematic block diagram illustrating a system for implementing the method of the invention for automated determination of skinline and skin thickening.
- radiographic images of an object are obtained from an image acquisition device 210 and input to the system.
- Each breast image is digitized by device 210 and put into memory 211.
- the image data is first passed through a segmentation circuit 212 and the gradient image producing circuit 213.
- the data is passed to an external skinline local optimization circuit 214 and the skin line determination circuit 215 in order to determine the internal and external skin lines.
- Data are passed to the skin analysis circuit 216 in order to determine skin thickening.
- the superimposing circuit 217 either the skinlines are superimposed onto breast images, stored in file format or output in terms of skin thickening.
- the results are then displayed on the display system 219 after passing through a digital-to-analog converter 218.
- the segmented breast image can also be used as input to the method for the automated detection of skin detection and skin thickening as shown in Figure 22.
- segmentation step 221) and identification of the external skinline (step 222)
- the Euclidean distance for each potential breast pixel to the external skinline is calculated (step 223) .
- the average gray value as a function of distance from the external skinline is examined and used in determining the enhancement factor (step 224) .
- This enhancement selectively enhances the peripheral region in order to simultaneously display the center of the breast and the skinline regions without loss in contrast .
- the trend can be corrected (step 225) and then displayed (step 226) .
- a graph showing the average gray values along a distance from the skinline is given in Figure 23.
- the gray values as a function of distance from the skinline are given before and after the enhancement method.
- the enhancement curve is obtained from a reversal of a fitted curve (such as a polynomial fit) to the average gray values (prior to enhancement) as a function of distance from the skinline. Constraints include the need for the fitted curve to have continuously smaller values, i.e. smaller gray values as distance increases .
- the values from the enhancement curve can be added to the corresponding pixels at the particular distance if the average gray value curve to produce the enhanced gray value curve. Other operations, besides addition, can also be used.
- FIG 24 is a more detailed schematic block diagram illustrating a system for implementing the method of the invention for automated enhancement of medical images.
- radiographic images of an object are obtained from an image acquisition device 230 and input to the system.
- Each breast image is digitized and put into memory 231.
- the image data is first passed through the segmentation circuit 232 and the external skinline identification circuit 233.
- the data is passed to the distance circuit 234 and the curve fitting circuit 235.
- Data are passed to the image enhancement circuit 236 in order to process the image.
- the processed image is then displayed on the display system 238 after passing through a digital-to-analog converter 237.
- the trend may also be corrected via trend correcting circuit 239.
Abstract
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Priority Applications (5)
Application Number | Priority Date | Filing Date | Title |
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EP95903128A EP0731959B1 (en) | 1993-11-29 | 1994-11-29 | Automated method and system for the processing of medical images |
AT95903128T ATE232325T1 (en) | 1993-11-29 | 1994-11-29 | AUTOMATIC METHOD AND SYSTEM FOR PROCESSING MEDICAL IMAGES |
JP7515142A JPH09508814A (en) | 1993-11-29 | 1994-11-29 | Automatic method and system for segmenting medical images |
DE69432106T DE69432106T2 (en) | 1993-11-29 | 1994-11-29 | AUTOMATIC METHOD AND SYSTEM FOR PROCESSING MEDICAL IMAGES |
AU12103/95A AU692499B2 (en) | 1993-11-29 | 1994-11-29 | Automated method and system for the segmentation of medical images |
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US08/158,320 US5452367A (en) | 1993-11-29 | 1993-11-29 | Automated method and system for the segmentation of medical images |
US158,320 | 1993-11-29 |
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WO1995014966A1 true WO1995014966A1 (en) | 1995-06-01 |
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EP (2) | EP0731959B1 (en) |
JP (1) | JPH09508814A (en) |
AT (2) | ATE232325T1 (en) |
AU (1) | AU692499B2 (en) |
CA (1) | CA2177477A1 (en) |
DE (2) | DE69432106T2 (en) |
WO (1) | WO1995014966A1 (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1996021198A1 (en) * | 1994-12-30 | 1996-07-11 | Philips Electronics N.V. | Automatic segmentation, skinline and nipple detection in digital mammograms |
EP0803843A2 (en) * | 1996-03-29 | 1997-10-29 | Teijin Limited | A method of processing a sectional image of a sample bone including a cortical bone portion and a cancellous bone portion |
WO2005057493A1 (en) * | 2003-12-10 | 2005-06-23 | Agency For Science, Technology And Research | Methods and apparatus for binarising images |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6266435B1 (en) | 1993-09-29 | 2001-07-24 | Shih-Ping Wang | Computer-aided diagnosis method and system |
US6574357B2 (en) | 1993-09-29 | 2003-06-03 | Shih-Ping Wang | Computer-aided diagnosis method and system |
US6075879A (en) * | 1993-09-29 | 2000-06-13 | R2 Technology, Inc. | Method and system for computer-aided lesion detection using information from multiple images |
EP0687373A1 (en) * | 1993-12-30 | 1995-12-20 | Koninklijke Philips Electronics N.V. | Automatic segmentation and skinline detection in digital mammograms |
US5583659A (en) * | 1994-11-10 | 1996-12-10 | Eastman Kodak Company | Multi-windowing technique for thresholding an image using local image properties |
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US5657362A (en) * | 1995-02-24 | 1997-08-12 | Arch Development Corporation | Automated method and system for computerized detection of masses and parenchymal distortions in medical images |
EP0813720A4 (en) * | 1995-03-03 | 1998-07-01 | Arch Dev Corp | Method and system for the detection of lesions in medical images |
US5807276A (en) * | 1995-03-09 | 1998-09-15 | Russin; Lincoln David | Biopsy device and method |
US5795308A (en) * | 1995-03-09 | 1998-08-18 | Russin; Lincoln D. | Apparatus for coaxial breast biopsy |
FR2733336A1 (en) * | 1995-04-20 | 1996-10-25 | Philips Electronique Lab | METHOD AND DEVICE FOR PROCESSING IMAGES FOR AUTOMATIC DETECTION OF OBJECTS IN DIGITAL IMAGES |
US6256529B1 (en) * | 1995-07-26 | 2001-07-03 | Burdette Medical Systems, Inc. | Virtual reality 3D visualization for surgical procedures |
US5889882A (en) * | 1996-03-21 | 1999-03-30 | Eastman Kodak Company | Detection of skin-line transition in digital medical imaging |
US5815591A (en) * | 1996-07-10 | 1998-09-29 | R2 Technology, Inc. | Method and apparatus for fast detection of spiculated lesions in digital mammograms |
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US5917929A (en) * | 1996-07-23 | 1999-06-29 | R2 Technology, Inc. | User interface for computer aided diagnosis system |
US5796862A (en) * | 1996-08-16 | 1998-08-18 | Eastman Kodak Company | Apparatus and method for identification of tissue regions in digital mammographic images |
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US5982916A (en) * | 1996-09-30 | 1999-11-09 | Siemens Corporate Research, Inc. | Method and apparatus for automatically locating a region of interest in a radiograph |
US5757880A (en) * | 1997-01-08 | 1998-05-26 | Colomb; Denis | Apparatus, article of manufacture, and method for creation of an uncompressed image of compressed matter |
US5859891A (en) * | 1997-03-07 | 1999-01-12 | Hibbard; Lyn | Autosegmentation/autocontouring system and method for use with three-dimensional radiation therapy treatment planning |
US6246782B1 (en) | 1997-06-06 | 2001-06-12 | Lockheed Martin Corporation | System for automated detection of cancerous masses in mammograms |
US5930327A (en) * | 1997-06-23 | 1999-07-27 | Trex Medical Corporation | X-ray image processing |
US6058322A (en) * | 1997-07-25 | 2000-05-02 | Arch Development Corporation | Methods for improving the accuracy in differential diagnosis on radiologic examinations |
US5984870A (en) * | 1997-07-25 | 1999-11-16 | Arch Development Corporation | Method and system for the automated analysis of lesions in ultrasound images |
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US6317617B1 (en) | 1997-07-25 | 2001-11-13 | Arch Development Corporation | Method, computer program product, and system for the automated analysis of lesions in magnetic resonance, mammogram and ultrasound images |
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US6246784B1 (en) | 1997-08-19 | 2001-06-12 | The United States Of America As Represented By The Department Of Health And Human Services | Method for segmenting medical images and detecting surface anomalies in anatomical structures |
US5999639A (en) | 1997-09-04 | 1999-12-07 | Qualia Computing, Inc. | Method and system for automated detection of clustered microcalcifications from digital mammograms |
US7308126B2 (en) * | 1997-08-28 | 2007-12-11 | Icad, Inc. | Use of computer-aided detection system outputs in clinical practice |
US6137898A (en) * | 1997-08-28 | 2000-10-24 | Qualia Computing, Inc. | Gabor filtering for improved microcalcification detection in digital mammograms |
US6970587B1 (en) | 1997-08-28 | 2005-11-29 | Icad, Inc. | Use of computer-aided detection system outputs in clinical practice |
GB9724110D0 (en) * | 1997-11-15 | 1998-01-14 | Elekta Ab | Analysis of radiographic images |
AU4318499A (en) | 1997-11-24 | 1999-12-13 | Burdette Medical Systems, Inc. | Real time brachytherapy spatial registration and visualization system |
US6996549B2 (en) | 1998-05-01 | 2006-02-07 | Health Discovery Corporation | Computer-aided image analysis |
WO1999057683A1 (en) | 1998-05-04 | 1999-11-11 | The Johns Hopkins University | Method and apparatus for segmenting small structures in images |
US6138045A (en) * | 1998-08-07 | 2000-10-24 | Arch Development Corporation | Method and system for the segmentation and classification of lesions |
US6381352B1 (en) * | 1999-02-02 | 2002-04-30 | The United States Of America As Represented By The Secretary Of The Navy | Method of isolating relevant subject matter in an image |
US6801645B1 (en) | 1999-06-23 | 2004-10-05 | Icad, Inc. | Computer aided detection of masses and clustered microcalcifications with single and multiple input image context classification strategies |
US6941323B1 (en) | 1999-08-09 | 2005-09-06 | Almen Laboratories, Inc. | System and method for image comparison and retrieval by enhancing, defining, and parameterizing objects in images |
US6898303B2 (en) | 2000-01-18 | 2005-05-24 | Arch Development Corporation | Method, system and computer readable medium for the two-dimensional and three-dimensional detection of lesions in computed tomography scans |
US6901156B2 (en) * | 2000-02-04 | 2005-05-31 | Arch Development Corporation | Method, system and computer readable medium for an intelligent search workstation for computer assisted interpretation of medical images |
US20030135102A1 (en) * | 2000-05-18 | 2003-07-17 | Burdette Everette C. | Method and system for registration and guidance of intravascular treatment |
US20020165839A1 (en) * | 2001-03-14 | 2002-11-07 | Taylor Kevin M. | Segmentation and construction of segmentation classifiers |
FR2825816B1 (en) * | 2001-06-08 | 2004-07-23 | Ge Med Sys Global Tech Co Llc | METHOD FOR DISPLAYING ORGAN IMAGES |
EP1412541A2 (en) * | 2001-06-20 | 2004-04-28 | Koninklijke Philips Electronics N.V. | Method for segmentation of digital images |
CN1612713A (en) | 2001-11-05 | 2005-05-04 | 计算机化医学体系股份有限公司 | Apparatus and method for registration, guidance, and targeting of external beam radiation therapy |
AUPR891701A0 (en) * | 2001-11-16 | 2001-12-13 | Proteome Systems Ltd | Method for locating the edge of an object |
US7123761B2 (en) * | 2001-11-20 | 2006-10-17 | Konica Corporation | Feature extracting method, subject recognizing method and image processing apparatus |
US7072498B1 (en) | 2001-11-21 | 2006-07-04 | R2 Technology, Inc. | Method and apparatus for expanding the use of existing computer-aided detection code |
US7054473B1 (en) | 2001-11-21 | 2006-05-30 | R2 Technology, Inc. | Method and apparatus for an improved computer aided diagnosis system |
US20030103663A1 (en) * | 2001-11-23 | 2003-06-05 | University Of Chicago | Computerized scheme for distinguishing between benign and malignant nodules in thoracic computed tomography scans by use of similar images |
GB0205000D0 (en) * | 2002-03-04 | 2002-04-17 | Isis Innovation | Unsupervised data segmentation |
US6707878B2 (en) * | 2002-04-15 | 2004-03-16 | General Electric Company | Generalized filtered back-projection reconstruction in digital tomosynthesis |
US7187800B2 (en) | 2002-08-02 | 2007-03-06 | Computerized Medical Systems, Inc. | Method and apparatus for image segmentation using Jensen-Shannon divergence and Jensen-Renyi divergence |
US7260250B2 (en) * | 2002-09-30 | 2007-08-21 | The United States Of America As Represented By The Secretary Of The Department Of Health And Human Services | Computer-aided classification of anomalies in anatomical structures |
US8565372B2 (en) | 2003-11-26 | 2013-10-22 | Hologic, Inc | System and method for low dose tomosynthesis |
US7616801B2 (en) | 2002-11-27 | 2009-11-10 | Hologic, Inc. | Image handling and display in x-ray mammography and tomosynthesis |
US8571289B2 (en) | 2002-11-27 | 2013-10-29 | Hologic, Inc. | System and method for generating a 2D image from a tomosynthesis data set |
US10638994B2 (en) | 2002-11-27 | 2020-05-05 | Hologic, Inc. | X-ray mammography with tomosynthesis |
US7577282B2 (en) | 2002-11-27 | 2009-08-18 | Hologic, Inc. | Image handling and display in X-ray mammography and tomosynthesis |
US7123684B2 (en) * | 2002-11-27 | 2006-10-17 | Hologic, Inc. | Full field mammography with tissue exposure control, tomosynthesis, and dynamic field of view processing |
FR2847698B1 (en) * | 2002-11-27 | 2005-05-06 | Ge Med Sys Global Tech Co Llc | METHOD FOR MANAGING THE DYNAMICS OF A DIGITAL RADIOLOGICAL IMAGE |
US20040122702A1 (en) * | 2002-12-18 | 2004-06-24 | Sabol John M. | Medical data processing system and method |
US20040122719A1 (en) * | 2002-12-18 | 2004-06-24 | Sabol John M. | Medical resource processing system and method utilizing multiple resource type data |
US20040122704A1 (en) * | 2002-12-18 | 2004-06-24 | Sabol John M. | Integrated medical knowledge base interface system and method |
US20040122708A1 (en) * | 2002-12-18 | 2004-06-24 | Avinash Gopal B. | Medical data analysis method and apparatus incorporating in vitro test data |
US20040122709A1 (en) * | 2002-12-18 | 2004-06-24 | Avinash Gopal B. | Medical procedure prioritization system and method utilizing integrated knowledge base |
US20040122706A1 (en) * | 2002-12-18 | 2004-06-24 | Walker Matthew J. | Patient data acquisition system and method |
US7187790B2 (en) * | 2002-12-18 | 2007-03-06 | Ge Medical Systems Global Technology Company, Llc | Data processing and feedback method and system |
US20040122705A1 (en) * | 2002-12-18 | 2004-06-24 | Sabol John M. | Multilevel integrated medical knowledge base system and method |
US7490085B2 (en) * | 2002-12-18 | 2009-02-10 | Ge Medical Systems Global Technology Company, Llc | Computer-assisted data processing system and method incorporating automated learning |
US20040122707A1 (en) * | 2002-12-18 | 2004-06-24 | Sabol John M. | Patient-driven medical data processing system and method |
US20040122787A1 (en) * | 2002-12-18 | 2004-06-24 | Avinash Gopal B. | Enhanced computer-assisted medical data processing system and method |
US20040122703A1 (en) * | 2002-12-19 | 2004-06-24 | Walker Matthew J. | Medical data operating model development system and method |
US7321669B2 (en) * | 2003-07-10 | 2008-01-22 | Sarnoff Corporation | Method and apparatus for refining target position and size estimates using image and depth data |
US7369638B2 (en) * | 2003-07-11 | 2008-05-06 | Siemens Medical Solutions Usa, Inc. | System and method for detecting a protrusion in a medical image |
US7515743B2 (en) * | 2004-01-08 | 2009-04-07 | Siemens Medical Solutions Usa, Inc. | System and method for filtering a medical image |
GB2412803A (en) * | 2004-03-30 | 2005-10-05 | Amersham Biosciences Niagara I | Image segmentation using local and remote pixel value comparison |
US20060018524A1 (en) * | 2004-07-15 | 2006-01-26 | Uc Tech | Computerized scheme for distinction between benign and malignant nodules in thoracic low-dose CT |
JP2006068373A (en) * | 2004-09-03 | 2006-03-16 | Fuji Photo Film Co Ltd | Mammilla detector and program thereof |
US7662082B2 (en) | 2004-11-05 | 2010-02-16 | Theragenics Corporation | Expandable brachytherapy device |
EP2602743B1 (en) | 2004-11-15 | 2014-11-05 | Hologic, Inc. | Matching geometry generation and display of mammograms and tomosynthesis images |
EP3106094B1 (en) | 2004-11-26 | 2021-09-08 | Hologic, Inc. | Integrated multi-mode mammography/tomosynthesis x-ray system |
US20060136259A1 (en) * | 2004-12-17 | 2006-06-22 | General Electric Company | Multi-dimensional analysis of medical data |
US20060136417A1 (en) * | 2004-12-17 | 2006-06-22 | General Electric Company | Method and system for search, analysis and display of structured data |
DE102004061507B4 (en) * | 2004-12-21 | 2007-04-12 | Siemens Ag | Method for correcting inhomogeneities in an image and imaging device therefor |
GB0510793D0 (en) * | 2005-05-26 | 2005-06-29 | Bourbay Ltd | Segmentation of digital images |
GB0510792D0 (en) * | 2005-05-26 | 2005-06-29 | Bourbay Ltd | Assisted selections with automatic indication of blending areas |
EP1908404A4 (en) * | 2005-07-27 | 2009-12-23 | Konica Minolta Med & Graphic | Abnormal shade candidate detection method and abnormal shade candidate detection device |
CN1907225B (en) * | 2005-08-05 | 2011-02-02 | Ge医疗系统环球技术有限公司 | Process and apparatus for dividing intracerebral hemorrhage injury |
US20070078873A1 (en) * | 2005-09-30 | 2007-04-05 | Avinash Gopal B | Computer assisted domain specific entity mapping method and system |
US10008184B2 (en) | 2005-11-10 | 2018-06-26 | Hologic, Inc. | System and method for generating a 2D image using mammography and/or tomosynthesis image data |
US8079946B2 (en) | 2005-11-18 | 2011-12-20 | Senorx, Inc. | Asymmetrical irradiation of a body cavity |
US7899514B1 (en) | 2006-01-26 | 2011-03-01 | The United States Of America As Represented By The Secretary Of The Army | Medical image processing methodology for detection and discrimination of objects in tissue |
EP1986548B1 (en) | 2006-02-15 | 2013-01-02 | Hologic, Inc. | Breast biopsy and needle localization using tomosynthesis systems |
US20070206844A1 (en) * | 2006-03-03 | 2007-09-06 | Fuji Photo Film Co., Ltd. | Method and apparatus for breast border detection |
DE102006021036B4 (en) * | 2006-04-28 | 2010-04-08 | Image Diagnost International Gmbh | Apparatus and method for computer aided analysis of mammograms |
DE102006021042A1 (en) * | 2006-04-28 | 2007-10-31 | Image Diagnost International Gmbh | Mammogram analyzing device, has unit for executing threshold value procedure, which uses threshold value, where device is designed and provided to determine contour line of mammogram based on image characteristics of mammogram |
US8184927B2 (en) * | 2006-07-31 | 2012-05-22 | Stc.Unm | System and method for reduction of speckle noise in an image |
US7961975B2 (en) * | 2006-07-31 | 2011-06-14 | Stc. Unm | System and method for reduction of speckle noise in an image |
DE102006046191B4 (en) | 2006-09-29 | 2017-02-02 | Siemens Healthcare Gmbh | Stray radiation correction in radiography and computed tomography with area detectors |
JP4833785B2 (en) * | 2006-09-29 | 2011-12-07 | 富士フイルム株式会社 | Radiographic apparatus and radiographic method |
US7940970B2 (en) | 2006-10-25 | 2011-05-10 | Rcadia Medical Imaging, Ltd | Method and system for automatic quality control used in computerized analysis of CT angiography |
US7983459B2 (en) | 2006-10-25 | 2011-07-19 | Rcadia Medical Imaging Ltd. | Creating a blood vessel tree from imaging data |
US7940977B2 (en) | 2006-10-25 | 2011-05-10 | Rcadia Medical Imaging Ltd. | Method and system for automatic analysis of blood vessel structures to identify calcium or soft plaque pathologies |
US7860283B2 (en) | 2006-10-25 | 2010-12-28 | Rcadia Medical Imaging Ltd. | Method and system for the presentation of blood vessel structures and identified pathologies |
US7873194B2 (en) | 2006-10-25 | 2011-01-18 | Rcadia Medical Imaging Ltd. | Method and system for automatic analysis of blood vessel structures and pathologies in support of a triple rule-out procedure |
US8086002B2 (en) * | 2007-04-27 | 2011-12-27 | Three Palm Software | Algorithms for selecting mass density candidates from digital mammograms |
US20090080773A1 (en) * | 2007-09-20 | 2009-03-26 | Mark Shaw | Image segmentation using dynamic color gradient threshold, texture, and multimodal-merging |
US7630533B2 (en) | 2007-09-20 | 2009-12-08 | Hologic, Inc. | Breast tomosynthesis with display of highlighted suspected calcifications |
US7792245B2 (en) * | 2008-06-24 | 2010-09-07 | Hologic, Inc. | Breast tomosynthesis system with shifting face shield |
US7991106B2 (en) | 2008-08-29 | 2011-08-02 | Hologic, Inc. | Multi-mode tomosynthesis/mammography gain calibration and image correction using gain map information from selected projection angles |
RU2530302C2 (en) * | 2008-10-29 | 2014-10-10 | Конинклейке Филипс Электроникс Н.В. | Analysis of, at least, three-dimensional medical image |
US8139832B2 (en) * | 2008-12-12 | 2012-03-20 | Hologic, Inc. | Processing medical images of the breast to detect anatomical abnormalities therein |
US9579524B2 (en) | 2009-02-11 | 2017-02-28 | Hologic, Inc. | Flexible multi-lumen brachytherapy device |
US9248311B2 (en) | 2009-02-11 | 2016-02-02 | Hologic, Inc. | System and method for modifying a flexibility of a brachythereapy catheter |
US10207126B2 (en) | 2009-05-11 | 2019-02-19 | Cytyc Corporation | Lumen visualization and identification system for multi-lumen balloon catheter |
WO2011007312A1 (en) * | 2009-07-17 | 2011-01-20 | Koninklijke Philips Electronics N.V. | Multi-modality breast imaging |
GB2474319B (en) * | 2009-07-20 | 2014-05-07 | Matakina Technology Ltd | Method and system for analysing tissue from images |
CN102549618B (en) * | 2009-08-03 | 2015-11-25 | 马塔基纳科技有限公司 | For the method and system from graphical analysis tissue |
WO2011043838A1 (en) | 2009-10-08 | 2011-04-14 | Hologic, Inc . | Needle breast biopsy system and method of use |
JP5468362B2 (en) * | 2009-11-18 | 2014-04-09 | 株式会社東芝 | Mammography equipment |
CN101866487B (en) * | 2010-06-11 | 2014-01-08 | 中国科学院深圳先进技术研究院 | Method and system for extracting body part from medical image |
US9352172B2 (en) | 2010-09-30 | 2016-05-31 | Hologic, Inc. | Using a guide member to facilitate brachytherapy device swap |
JP5955327B2 (en) | 2010-10-05 | 2016-07-20 | ホロジック, インコーポレイテッドHologic, Inc. | System and method for x-ray imaging of an upright patient's breast |
US9075903B2 (en) | 2010-11-26 | 2015-07-07 | Hologic, Inc. | User interface for medical image review workstation |
WO2012082861A2 (en) | 2010-12-14 | 2012-06-21 | Hologic, Inc. | System and method for fusing three dimensional image data from a plurality of different imaging systems for use in diagnostic imaging |
US8861814B2 (en) * | 2010-12-22 | 2014-10-14 | Chevron U.S.A. Inc. | System and method for multi-phase segmentation of density images representing porous media |
US10342992B2 (en) | 2011-01-06 | 2019-07-09 | Hologic, Inc. | Orienting a brachytherapy applicator |
WO2012109643A2 (en) * | 2011-02-11 | 2012-08-16 | Emory University | Systems, methods and computer readable storage mediums storing instructions for classifying breast ct images |
CN103477346A (en) | 2011-03-08 | 2013-12-25 | 霍洛吉克公司 | System and method for dual energy and/or contrast enhanced breast imaging for screening, diagnosis and biopsy |
US8600107B2 (en) * | 2011-03-31 | 2013-12-03 | Smart Technologies Ulc | Interactive input system and method |
FR2977054A1 (en) * | 2011-06-23 | 2012-12-28 | St Microelectronics Grenoble 2 | METHOD FOR IMPROVING THE VISUAL PERCEPTION OF A DIGITAL IMAGE |
US8781187B2 (en) * | 2011-07-13 | 2014-07-15 | Mckesson Financial Holdings | Methods, apparatuses, and computer program products for identifying a region of interest within a mammogram image |
KR102109588B1 (en) | 2011-11-27 | 2020-05-12 | 홀로직, 인크. | Methods for processing, displaying and navigating breast tissue images |
US9805507B2 (en) | 2012-02-13 | 2017-10-31 | Hologic, Inc | System and method for navigating a tomosynthesis stack using synthesized image data |
CN103093449A (en) * | 2013-02-28 | 2013-05-08 | 重庆大学 | Multi-resolution fusion radial image enhancement method |
JP6388347B2 (en) | 2013-03-15 | 2018-09-12 | ホロジック, インコーポレイテッドHologic, Inc. | Tomosynthesis guided biopsy in prone position |
CN104367331B (en) * | 2013-08-15 | 2017-02-15 | 深圳市蓝韵实业有限公司 | Full-digital automatic exposure method for digital mammary gland X-ray machine |
EP3035850B1 (en) | 2013-08-20 | 2020-05-13 | Densitas Incorporated | Methods and systems for determining breast density |
EP3055837B1 (en) | 2013-10-09 | 2021-08-04 | Hologic, Inc. | X-ray breast tomosynthesis enhancing spatial resolution including in the thickness direction of a flattened breast |
US9443161B2 (en) * | 2014-02-24 | 2016-09-13 | Dimensions And Shapes, Llc | Methods and systems for performing segmentation and registration of images using neutrosophic similarity scores |
JP6506769B2 (en) | 2014-02-28 | 2019-04-24 | ホロジック, インコーポレイテッドHologic, Inc. | System and method for generating and displaying tomosynthesis image slabs |
US9626476B2 (en) | 2014-03-27 | 2017-04-18 | Change Healthcare Llc | Apparatus, method and computer-readable storage medium for transforming digital images |
CN104680537B (en) * | 2015-03-09 | 2018-05-15 | 苏州比特速浪电子科技有限公司 | Image processing apparatus |
CN108471995B (en) * | 2015-09-30 | 2022-03-29 | 上海联影医疗科技股份有限公司 | System and method for determining breast regions in medical images |
CN115049563A (en) * | 2015-12-31 | 2022-09-13 | 上海联影医疗科技股份有限公司 | Image processing method and system |
EP3445247B1 (en) | 2016-04-22 | 2021-03-10 | Hologic, Inc. | Tomosynthesis with shifting focal spot x-ray system using an addressable array |
WO2017205386A1 (en) | 2016-05-27 | 2017-11-30 | Hologic, Inc. | Synchronized surface and internal tumor detection |
US20190122397A1 (en) | 2016-07-12 | 2019-04-25 | Mindshare Medical, Inc. | Medical analytics system |
US11246551B2 (en) | 2016-09-20 | 2022-02-15 | KUB Technologies, Inc. | System and method for computer aided detection (CAD) in a breast specimen radiograph |
JP7169986B2 (en) | 2017-03-30 | 2022-11-11 | ホロジック, インコーポレイテッド | Systems and methods for synthesizing low-dimensional image data from high-dimensional image data using object grid augmentation |
WO2018183548A1 (en) | 2017-03-30 | 2018-10-04 | Hologic, Inc. | System and method for hierarchical multi-level feature image synthesis and representation |
JP7174710B2 (en) | 2017-03-30 | 2022-11-17 | ホロジック, インコーポレイテッド | Systems and Methods for Targeted Object Augmentation to Generate Synthetic Breast Tissue Images |
EP3641635A4 (en) | 2017-06-20 | 2021-04-07 | Hologic, Inc. | Dynamic self-learning medical image method and system |
WO2019035064A1 (en) | 2017-08-16 | 2019-02-21 | Hologic, Inc. | Techniques for breast imaging patient motion artifact compensation |
EP3449835B1 (en) | 2017-08-22 | 2023-01-11 | Hologic, Inc. | Computed tomography system and method for imaging multiple anatomical targets |
US11049606B2 (en) | 2018-04-25 | 2021-06-29 | Sota Precision Optics, Inc. | Dental imaging system utilizing artificial intelligence |
US11090017B2 (en) | 2018-09-13 | 2021-08-17 | Hologic, Inc. | Generating synthesized projection images for 3D breast tomosynthesis or multi-mode x-ray breast imaging |
EP3832689A3 (en) | 2019-12-05 | 2021-08-11 | Hologic, Inc. | Systems and methods for improved x-ray tube life |
US11471118B2 (en) | 2020-03-27 | 2022-10-18 | Hologic, Inc. | System and method for tracking x-ray tube focal spot position |
US11786191B2 (en) | 2021-05-17 | 2023-10-17 | Hologic, Inc. | Contrast-enhanced tomosynthesis with a copper filter |
CN117635609B (en) * | 2024-01-25 | 2024-03-29 | 深圳市智宇精密五金塑胶有限公司 | Visual inspection method for production quality of plastic products |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5268967A (en) * | 1992-06-29 | 1993-12-07 | Eastman Kodak Company | Method for automatic foreground and background detection in digital radiographic images |
Family Cites Families (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4581636A (en) * | 1984-04-02 | 1986-04-08 | Advanced Technology Laboratories, Inc. | Scan conversion apparatus and method |
US4731863A (en) * | 1986-04-07 | 1988-03-15 | Eastman Kodak Company | Digital image processing method employing histogram peak detection |
US4907156A (en) * | 1987-06-30 | 1990-03-06 | University Of Chicago | Method and system for enhancement and detection of abnormal anatomic regions in a digital image |
US4952805A (en) * | 1987-08-20 | 1990-08-28 | Fuji Photo Film Co., Ltd. | Method of judging the presence or absence of a limited irradiation field, method of selecting a correct irradiation field, and method of judging correctness or incorrectness of an irradiation field |
DE68927031T2 (en) * | 1988-09-19 | 1997-01-23 | Fuji Photo Film Co Ltd | Method for determining the desired areas of an image signal and method for determining the desired image areas |
US5133020A (en) * | 1989-07-21 | 1992-07-21 | Arch Development Corporation | Automated method and system for the detection and classification of abnormal lesions and parenchymal distortions in digital medical images |
CA2014918A1 (en) * | 1989-09-06 | 1991-03-06 | James A. Mcfaul | Scanning mammography system with improved skin line viewing |
JP2845995B2 (en) * | 1989-10-27 | 1999-01-13 | 株式会社日立製作所 | Region extraction method |
US5164993A (en) * | 1991-11-25 | 1992-11-17 | Eastman Kodak Company | Method and apparatus for automatic tonescale generation in digital radiographic images |
-
1993
- 1993-11-29 US US08/158,320 patent/US5452367A/en not_active Expired - Lifetime
-
1994
- 1994-11-29 AT AT95903128T patent/ATE232325T1/en not_active IP Right Cessation
- 1994-11-29 WO PCT/US1994/013281 patent/WO1995014966A1/en active IP Right Grant
- 1994-11-29 AT AT00111213T patent/ATE246383T1/en not_active IP Right Cessation
- 1994-11-29 DE DE69432106T patent/DE69432106T2/en not_active Expired - Lifetime
- 1994-11-29 CA CA002177477A patent/CA2177477A1/en not_active Abandoned
- 1994-11-29 JP JP7515142A patent/JPH09508814A/en not_active Ceased
- 1994-11-29 EP EP95903128A patent/EP0731959B1/en not_active Expired - Lifetime
- 1994-11-29 AU AU12103/95A patent/AU692499B2/en not_active Ceased
- 1994-11-29 DE DE69432995T patent/DE69432995T2/en not_active Expired - Lifetime
- 1994-11-29 EP EP00111213A patent/EP1035508B1/en not_active Expired - Lifetime
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5268967A (en) * | 1992-06-29 | 1993-12-07 | Eastman Kodak Company | Method for automatic foreground and background detection in digital radiographic images |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1996021198A1 (en) * | 1994-12-30 | 1996-07-11 | Philips Electronics N.V. | Automatic segmentation, skinline and nipple detection in digital mammograms |
US5572565A (en) * | 1994-12-30 | 1996-11-05 | Philips Electronics North America Corporation | Automatic segmentation, skinline and nipple detection in digital mammograms |
EP0803843A2 (en) * | 1996-03-29 | 1997-10-29 | Teijin Limited | A method of processing a sectional image of a sample bone including a cortical bone portion and a cancellous bone portion |
EP0803843A3 (en) * | 1996-03-29 | 1997-11-12 | Teijin Limited | A method of processing a sectional image of a sample bone including a cortical bone portion and a cancellous bone portion |
US5835619A (en) * | 1996-03-29 | 1998-11-10 | Teijin Limited | Method of processing a sectional image of a sample bone including a cortical bone portion and a cancellous bone portion |
WO2005057493A1 (en) * | 2003-12-10 | 2005-06-23 | Agency For Science, Technology And Research | Methods and apparatus for binarising images |
EP1577835A2 (en) * | 2004-03-17 | 2005-09-21 | Canon Kabushiki Kaisha | X-ray image processing apparatus and method |
EP1577835A3 (en) * | 2004-03-17 | 2007-06-06 | Canon Kabushiki Kaisha | X-ray image processing apparatus and method |
US7418122B2 (en) | 2004-03-17 | 2008-08-26 | Canon Kabushiki Kaisha | Image processing apparatus and method |
EP2161688A1 (en) * | 2008-09-03 | 2010-03-10 | Agfa Healthcare | Method for deriving the amount of dense tissue from a digital mammographic image representation |
US8428330B2 (en) | 2008-09-03 | 2013-04-23 | Agfa Healthcare Nv | Method for deriving amount of dense tissue from mammographic image |
CN111260631A (en) * | 2020-01-16 | 2020-06-09 | 成都地铁运营有限公司 | Efficient rigid contact line structure light strip extraction method |
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DE69432995D1 (en) | 2003-09-04 |
ATE246383T1 (en) | 2003-08-15 |
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