16 April 1996 Detecting circumscribed lesions with the Hough transform
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We have designed and implemented a circumscribed lesion detection algorithm, based on the Hough Transform, which will detect zero or more approximately circular structures in a mammogram over a range of radii from a few pixels to nearly the size of the breast. We address the geometrical behavior of peaks in Hough parameter space {x, y, r} for both the true radius of a circular structure in the image (r equals ro), and for the parameter r as it passes through this radius. In addition, we scale the peaks in Hough parameter space by re-analyzing the contrast of the region in the mammogram underlying the circular disk indicated by the peak. These scaled peaks are mapped to circular disks and accumulated in an output feature image. This process defines a continuously scaled pixel level output. That output, a local image measurement or 'feature,' suggests the likelihood that a pixel is located inside a circular structure, irrespective of the radius of the structure and overall mammogram contrast. These feature values are evaluated by fast qualitative and quantitative performance metrics which permit circumscribed lesion detection features to be initially evaluated without a full end-to-end classification experiment.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bennett R. Groshong, Bennett R. Groshong, W. Philip Kegelmeyer, W. Philip Kegelmeyer, } "Detecting circumscribed lesions with the Hough transform", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237982; https://doi.org/10.1117/12.237982

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