Adaptive Gaussian Mixture Model-Based Statistical Feature Extraction for Computer-Aided Diagnosis of Micro-Calcification Clusters in Mammograms. Issue 4 (1st July 2020)

• Record Type: Journal Article
• Authors: Zhang, Zhang; Zhang, Xiaoyong; Ichiji, Kei; Takane, Yumi; Yanagaki, Satoru; Kawasumi, Yusuke; Ishibashi, Tadashi; Homma, Noriyasu
• Is Part Of: SICE journal of control, measurement, and system integration. Volume 13:Issue 4(2020)
• Page Start: 183
• Page End: 190
• Publication Date: 2020-07-01
• Subjects: mammography -- computer-aided diagnosis (CAD) -- micro-calcification clusters (MCCs) -- Gaussian mixture model (GMM)
• DOI: 10.9746/jcmsi.13.183
• Languages: English
• ISSNs: 1882-4889

Abstract

Abstract : In mammography, detection and categorization of micro-calcification clusters (MCCs) using computer-aided diagnosis (CAD) systems are very important tasks because MCCs are important signs at an early stage of breast cancer. However, the conventional methods of CAD only classify MCCs into benign and malignant types, and no method has been developed for a medical requirement to classify the MCCs into more detailed categories according to the spatial distribution of MCCs. To provide a cogent second opinion, we specifically focus on analyzing MCCs' spatial distribution and propose an adaptive Gaussian mixture model-based method to extract the statistical features of the spatial distribution in this study. By mimicking the radiologists' workflow, the proposed method used the main feature of each spatial distributions to classify the MCCs and then provide a cogent second opinion to increase the confidence level of diagnosis decisions. The experiments have been performed on 100 mammographic images with MCCs from a clinical dataset. The experimental results showed that the proposed method was able to detect the MCCs and classify the spatial distribution of the MCCs effectively.