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Breast Tumor Segmentation

Presentation Overview. Background and problem descriptionPrevious workOur approachResultsConclusion. Background. Ultrasonic strain imagingA strain image is a spatial map of local deformation that occurs because of an applied loadObtained by comparing a pre-compression image to a post-compressi

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Breast Tumor Segmentation

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Presentation Transcript

    1. Breast Tumor Segmentation

    2. Presentation Overview Background and problem description Previous work Our approach Results Conclusion

    3. 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

    4. The Problem Quantify contrast between tumor and background Must define tumor region and background region

    5. Previous work Parametric active contours, aka snakes

    6. Our Approach 1.) Smoothing filter 2.) Threshold 3.) Multiple morphological processing steps

    7. Step by Step

    8. 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

    9. Some Results

    10. Limitations

    11. Conclusions Advantages Very accurate and precise Robust Disadvantages Loops in MatLab slow

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