Modelling mammographic microcalcification clusters using persistent mereotopology

AbstractIn mammographic imaging, the presence of microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify such microcalcification clusters. In this paper a novel method for classifying microcalcification clusters is presented by representing discrete mereotopological relations between the individual microcalcifications over a range of scales in the form of a mereotopological barcode. This barcode based representation is able to model complex relations between multiple regions and the results on mammographic microcalcification data shows the effectiveness of this approach. Classification accuracies of 95% and 80% are achieved on the MIAS and DDSM datasets, respectively. These results are comparable to existing state-of-the art methods. This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases

AN AUTOMATED COMPUTER-AIDED DETECTION (CADe) AND DIAGNOSIS (CADx) SYSTEM FOR BREAST MICROCALCIFICATIONS IN MAMMOGRAMS

ABSTRACTAn automated computer aided diagnosis system has been proposed for detection of microcalcification (MC) clusters in mammograms. The proposed system is a whole system including suspicious regions identification, MCs detection, false positive reduction and benign/malign classification. For classification of suspicious microcalcification regions, a multilayer perceptron (MLP) neural network was used with grey level co-occurrence matrix (GLCM) and statistical features.  Then to decrease the false positive classification ratio, we used cascade correlation neural network (CCNN) with grey level run length matrix (GLRLM) features. In the last step, hybrid form of discriminant analysis and support vector machine (SVM) methods were used with GLRLM features for benign/malign classification of detected MC clusters. The open access Mammographic Image Analysis Society (MIAS) database was used for the study. Experimental results show that the proposed algorithm obtained 86% sensitivity, 98.3% specificity and 1.163 FPpI rates for detection an for diagnosis of breast cancer, the obtained sensitivity and specificity values are 100% and 100% respectively. Despite the vision difficulty of MC clusters, the novel system provides very satisfactory results. Furthermore, the developed system is fully automatic whole system which gives outputs as percentages and transformed assessment categories. Keywords: Mammograms, Breast cancer, Computer aided diagnosis, Cascade correlation neural network (CCNN), Grey level co-occurrence matrix (GLCM), Grey level run length matrix (GLRLM).