title: Detecting microcalcifications in digital mammograms using wavelet domain hidden Markov tree model.
abstract: In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov tree model (WHMT) for its inclusion to a computer-aided diagnostic prompting system for detecting microcalcification (MC) clusters. The system incorporates: (1) gross-segmentation of mammograms for obtaining the breast region; (2) eliminating the pepper-type noise, (3) block-wise wavelet transform of the breast signal and likelihood calculation; (4) image segmentation; (5) postprocessing for retaining MC clusters. FROC curves are obtained for all MC clusters containing mammograms of mini-MIAS database. 100% of true positive cases are detected by the system at 2.9 false positives per case.
authors.affiliation: Dept. of Electr. & Comput. Eng., Nevada Univ., Las Vegas, NV 89154, USA. regent@ee.unlv.edu
authors.forename: Emma
authors.initials: E
authors.lastname: Regentova
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.forename: Jun
authors.initials: J
authors.lastname: Zheng
authors.forename: Gopaalkrishna
authors.initials: G
authors.lastname: Veni
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008327:Mammography / Q000379:methods*; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011230:Precancerous Conditions / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted