Improving the accuracy in detection of clustered microcalcifications with a context‐sensitive classification model. Issue 1 (22nd December 2015)

• Record Type: Journal Article
• Authors: Wang, Juan; Nishikawa, Robert M.; Yang, Yongyi
• Is Part Of: Medical physics. Volume 43:Issue 1(2016)
• Page Start: 159
• Page End: 170
• Publication Date: 2015-12-22
• Subjects: cancer -- feature extraction -- image classification -- learning (artificial intelligence) -- mammography -- medical image processing -- support vector machines; Digital mammography; Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Inference methods or devices; computer‐aided detection (CADe) -- clustered microcalcifications (MCs) -- false positives -- mammography; Image sensors -- Monte Carlo methods -- Optical microcavities -- Film mammography -- Cancer -- Image analysis -- Machine learning -- Testing procedures; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4938059
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: In computer‐aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. Methods: To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference‐of‐Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free‐response receiver operating characteristic (FROC) analysis on a set of 141 screen‐film mammogram (SFM) images (66 cases) and a set of 188 full‐field digital mammogram (FFDM) images (95 cases). Results: The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true‐positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. Conclusions: The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (such as MC‐like noise patterns and linear structures) in MC detection. The framework is general and can be applicable for further improving the detection accuracy of existing MC detectors.