Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification.
This paper presents segmentation and classification results of an automated algorithm for the detection of breast masses on digitized mammograms. Potential mass regions were first identified using density-weighted contrast enhancement (DWCE) segmentation applied to single-view mammograms. Once the potential mass regions had been identified, multiresolution texture features extracted from wavelet coefficients were calculated, and linear discriminant analysis (LDA) was used to classify the regions as breast masses or normal tissue. In this article the overall detection results for two independent sets of 84 mammograms used alternately for training and test were evaluated by free-response receiver operating characteristics (FROC) analysis. The test results indicate that this new algorithm produced approximately 4.4 false positive per image at a true positive detection rate of 90% and 2.3 false positives per image at a true positive rate of 80%.
Petrick
N
N
University of Michigan, Department of Radiology, Ann Arbor 48109-0030, USA.
Chan
H P
HP
Wei
D
D
Sahiner
B
B
Helvie
M A
MA
Adler
D D
DD
D001331:Automation; D001940:Breast / Q000166:cytology* / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007256:Information Systems; D008327:Mammography; D011857:Radiographic Image Interpretation, Computer-Assisted; D012016:Reference Values