Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection

• Published in: Technology and health care Vol. 25; no. 1_suppl; pp. 325 - 336
• Main Authors: Wang, Huiya, Feng, Jun, Wang, Hongyu
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2017; Sage Publications Ltd
• Subjects: Algorithms; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Calcinosis - diagnosis; Calcinosis - diagnostic imaging; Calcinosis - pathology; Classification; Data structures; Female; Fuzzy Logic; Humans; Lesions; Mammography; Mammography - methods; Models, Statistical; Partitions; Radiographic Image Interpretation, Computer-Assisted - methods; Support Vector Machine; Support vector machines; Tissues; partition of sample space; grouped fuzzy SVM; EM algorithm; Pattern classification; computer aided detection
• ISSN: 0928-7329; 1878-7401; 1878-7401
• DOI: 10.3233/THC-171336

BACKGROUND: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. OBJECTIVE: To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. METHODS: In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. RESULTS: From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1 - FPR are 0.82, 0.78, 0.14 and 0.72, respectively. CONCLUSION: The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.