title: Comparative analysis of breast cancer detection in mammograms and thermograms.
abstract: In this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.
authors.forename: Marina
authors.initials: M
authors.lastname: Milosevic
authors.forename: Dragan
authors.initials: D
authors.lastname: Jankovic
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Peulic
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008327:Mammography / Q000379:methods*; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013817:Thermography / Q000379:methods*