title: Microcalcification detection based on wavelet domain hidden markov tree model: study for inclusion to computer aided diagnostic prompting system.
abstract: In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov trees for its inclusion to a computer-aided diagnostic prompting system. The system is designed for detecting clusters of microcalcifications. Their further discrimination as benign or malignant is to be done by radiologists. The model is used for segmenting images based on the maximum likelihood classifier enhanced by the weighting technique. Further classification incorporates spatial filtering for a single microcalcification (MC) and microcalcification cluster (MCC) detection. Contrast filtering applied for the digital database for screening mammography (DDSM) dataset prior to spatial filtering greatly improves the classification accuracy. For all MC clusters of 40 mammograms from the mini-MIAS database of Mammographic Image Analysis Society, 92.5%-100% of true positive cases can be detected under 2-3 false positives per image. For 150 cases of DDSM cases, the designed system is capable to detect up to 98% of true positives under 3.3% of false positive cases.
authors.affiliation: Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, Nevada 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: Gopalkrishna
authors.initials: G
authors.lastname: Veni
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008390:Markov Chains; 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; D014584:User-Computer Interface