Detecting microcalcifications in digital mammograms using wavelet domain hidden Markov tree model.
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.
Regentova
Emma
E
Dept. of Electr. & Comput. Eng., Nevada Univ., Las Vegas, NV 89154, USA. regent@ee.unlv.edu
Zhang
Lei
L
Zheng
Jun
J
Veni
Gopaalkrishna
G
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