title: Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms.
abstract: Mammograms are difficult to interpret, especially of cancers at their early stages. In this paper, we analyze the effectiveness of our adaptive neighborhood contrast enhancement (ANCE) technique in increasing the sensitivity of breast cancer diagnosis. Seventy-eight screen-film mammograms of 21 difficult cases (14 benign and seven malignant), 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a high-resolution of about 4096 x 2048 x 10-bit pixels and then processed with the ANCE method. Unprocessed and processed digitized mammograms as well as the original films were presented to six experienced radiologists for a receiver operating characteristic (ROC) evaluation for the difficult case set and to three reference radiologists for the interval cancer set. The results show that the radiologists' performance with the ANCE-processed images is the best among the three sets of images (original, digitized, and enhanced) in terms of area under the ROC curve and that diagnostic sensitivity is improved by the ANCE algorithm. All of the 19 interval cancer cases not detected with the original films of earlier mammographic examinations were diagnosed as malignant with the corresponding ANCE-processed versions, while only one of the six benign cases initially labeled correctly with the original mammograms was interpreted as malignant after enhancement. McNemar's tests of symmetry indicated that the diagnostic confidence for the interval cancer cases was improved by the ANCE procedure with a high level of statistical significance (p-values of 0.0001-0.005) and with no significant effect on the diagnosis of the benign control cases (p-values of 0.08-0.1). This study demonstrates the potential for improvement of diagnostic performance in early detection of breast cancer with digital image enhancement.
authors.affiliation: Department of Electrical and Computer Engineering, University of Calgary, Alta., Canada.
authors.forename: R M
authors.initials: RM
authors.lastname: Rangayyan
authors.forename: L
authors.initials: L
authors.lastname: Shen
authors.forename: Y
authors.initials: Y
authors.lastname: Shen
authors.forename: J E
authors.initials: JE
authors.lastname: Desautels
authors.forename: H
authors.initials: H
authors.lastname: Bryant
authors.forename: T J
authors.initials: TJ
authors.lastname: Terry
authors.forename: N
authors.initials: N
authors.lastname: Horeczko
authors.forename: M S
authors.initials: MS
authors.lastname: Rose
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods* / Q000706:statistics & numerical data; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012680:Sensitivity and Specificity