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UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 1 Breast Tumor Segmentation.

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Presentation on theme: "UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 1 Breast Tumor Segmentation."— Presentation transcript:

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2 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 1 Breast Tumor Segmentation

3 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 2 Breast Tumor Segmentation Presentation Overview Background and problem description Previous work Our approach Results Conclusion

4 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 3 Breast Tumor Segmentation Background Ultrasonic strain imaging A strain image is a spatial map of local deformation that occurs because of an applied load Obtained by comparing a pre-compression image to a post- compression image Tumors are stiff they show up as dark areas

5 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 4 Breast Tumor Segmentation The Problem Quantify contrast between tumor and background Must define tumor region and background region

6 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 5 Breast Tumor Segmentation Previous work Parametric active contours, aka snakes Strain Image Manual Segmentation by Radiologist Snakes Segmentation Wu Liu et. al. Segmentation of Elastographic Images using a Coarse-to-Fine Active Contour Model. Ultrasound in Medicine and Biology. Publication in progress.

7 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 6 Breast Tumor Segmentation Our Approach 1.) Smoothing filter 2.) Threshold 3.) Multiple morphological processing steps

8 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 7 Breast Tumor Segmentation Step by Step Original imageAveraging filter Threshold Remove holes Opening, remove fingers Isolate tumor Reverse opening distortion Result

9 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 8 Breast Tumor Segmentation Tumor Finding Three options for finding tumor: user-supplied coordinates, manual input, and automatic tumor finding. Automatic tumor finding: 1) Find the distance of each pixel from a black (0) pixel 2) Mark the pixel farthest from a black pixel and closest to the center of the image as inside the tumor

10 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 9 Breast Tumor Segmentation Some Results

11 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 10 Breast Tumor Segmentation Limitations

12 UW Madison Digital Image Processing Adam Slater and Matt McCormick / 12/14/2005 / Page 11 Breast Tumor Segmentation Conclusions Advantages Very accurate and precise Robust Disadvantages Loops in MatLab slow

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