Texture feature extraction for tumor detection in mammographic images

TitleTexture feature extraction for tumor detection in mammographic images
Publication TypeConference Paper
Year of Publication1997
AuthorsSameti, M., R. K. Ward, B. Palcic, and J. Morgan-Parkes
Conference NameCommunications, Computers and Signal Processing, 1997. '10 Years PACRim 1987-1997 - Networking the Pacific Rim'. 1997 IEEE Pacific Rim Conference on
Pagination831 -834 vol.2
Date Publishedaug.
Keywordscomputer aided diagnosis, diagnostic radiography, digitized mammograms, false-positives, feature extraction, fuzzy set theory, fuzzy sets theory, image classification, image segmentation, image texture, malignant mass, mammographic images, mass detection algorithm, masses classification, medical image processing, normal regions, segmentation method, segmented regions, texture feature extraction, true-positive detection rate, tumor detection

A set of texture features are extracted from segmented regions of digitized mammograms for classification of masses from normal regions. The mass detection algorithm consists of two steps. In the first step, the algorithm employs a segmentation method based on the fuzzy sets theory to divide a mammogram into different regions and produces region(s) of mass candidates. In the second step, discrete texture features are calculated for the area of each mass candidate. Two of those feature were sufficient to produce a 94% true-positive detection rate with a low 0.24 false-positives per image for a data set of 35 mammograms with a malignant mass in each


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