A new approach to develop computer-aided detection schemes of digital mammograms

Abstract

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a Sequential Forward Floating Selection (SFFS) feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC=0.779±0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

“…We recently developed a case based CAD scheme that computed global mammographic density based features from four-view (CC and MLO) mammograms (Tan et al , 2015b; Tan et al , 2014), and fused the features using a novel “scoring fusion” classifier. In the initial schemes, we had computed the simpler gray level and density based features, including mean, standard deviation, skewness, and run length statistics (RLS) features (Galloway, 1975).…”

“…Recently, we proposed a different approach to improve the efficacy of CAD for mammography by developing a case based CAD scheme (Tan et al , 2014; Tan et al , 2015b). Namely, by computing global mammographic density based image features from all four craniocaudal (CC) and mediolateral oblique (MLO) view mammograms, we trained a classifier to analyze the bilateral global mammographic image features and their differences to generate a likelihood score of the case in question being positive for cancer.…”

“…Thus, in this study, we hypothesize that the performance of the lesion based CAD schemes may be improved by combining supplementary information derived from our global bilateral mammographic density feature analysis based CAD scheme (Tan et al , 2015b; Tan et al , 2014). This was observed in our preliminary studies on a smaller dataset (Tan et al , 2016a).…”

A new paradigm of dielectric relaxation spectroscopy for non-invasive detection of breast abnormalities: a preliminary feasibility analysis

“…However, the efficacy of a uniform population-based mammography screening paradigm is very low and quite controversial with cancer detection yields less than 0.5% as well as false-positive recall rates around or higher than 10% [1][2][3]. Thus, in order to improve efficacy of current breast cancer screening methods (including mammography), developing and establishing a new and better, personalized, paradigm for breast-cancer screening has been attracting wide interest in the research community [4][5][6]. The basic goal is to develop an accurate prescreening-tool and/or risk prediction model to stratify women into two groups namely, high and low risk for having or developing mammography-detectable cancers in a short (or near) term.…”

Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches

“…The new CAD scheme focuses on detection and quantitative analysis of global mammographic image feature distribution and difference among all four-view images of the left and right breast. The scheme then generated a casebased likelihood score of being high risk for positive [20]. Using the case-based cancer risk scores, one can implement an adaptive CAD cueing method to increase sensitivity of cueing more subtle cancers without increase of false-positive detection rates [21].…”