13 October 1997 Automated microcalcification detection in mammograms using statistical variable-box-threshold filter method
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Abstract
Currently early detection of breast cancer is primarily accomplished by mammography and suspicious findings may lead to a decision for performing a biopsy. Digital enhancement and pattern recognition techniques may aid in early detection of some patterns such as microcalcification clusters indicating onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers and could be treated when detected early. These individual calcifications are hard to detect due to size and shape variability and inhomogeneous background texture. Our study addresses only early detection of microcalcifications that allows the radiologist to interpret the x-ray findings in computer-aided enhanced form easier than evaluating the x-ray film directly. We present an algorithm which locates microcalcifications based on local grayscale variability and of tissue structures and image statistics. Threshold filters with lower and upper bounds computed from the image statistics of the entire image and selected subimages were designed to enhance the entire image. This enhanced image was used as the initial image for identifying the micro-calcifications based on the variable box threshold filters at different resolutions. The test images came from the Texas Tech University Health Sciences Center and the MIAS mammographic database, which are classified into various categories including microcalcifications. Classification of other types of abnormalities in mammograms based on their characteristic features is addressed in later studies.
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Mark Wilson, Sunanda Mitra, Glenn H. Roberson, Yao-Yang Shieh, "Automated microcalcification detection in mammograms using statistical variable-box-threshold filter method", Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); doi: 10.1117/12.284214; https://doi.org/10.1117/12.284214
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