An approach to the detection of lesions in mammograms using fuzzy image processing.
An algorithm was developed in this study, using rule-based fuzzy logic, to enable masses that are hard to recognize or detect in mammograms to become more readily perceptible. Small lesions, such as microcalcifications and other masses that are hard to recognize, especially on film scan mammograms, were processed through segmentation. A total of 40 mammograms were used and they were classified by radiologists into three groups: those with microcalcifications (n=15), those with tumours (n=15), and those with no lesions (n=10). Five mammograms were taken as training data sets from each of the groups with microcalcifications and tumours. The algorithm was then applied to data not taken for training. The algorithm achieved a mean accuracy of 99% compared with the findings of the radiologists.
Division of Photogrammetry and Remote Sensing, Department of Geodesy and Photogrammetry, Faculty of Civil Engineering, Yildiz Technical University, Beşiktaş, Istanbul, Turkey. bulentbayram65@gmail.com
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D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000473:pathology; D005189:False Positive Reactions; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008327:Mammography / Q000379:methods*; D008403:Mass Screening; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*