Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

• Published in: Computer methods and programs in biomedicine Vol. 112; no. 1; pp. 38 - 46
• Main Author: ICER, Semra
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01-10-2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Brain; Computer science; control theory; systems; Computer simulation; Corpus collasum; Data processing. List processing. Character string processing; Exact sciences and technology; Fuzzy; Fuzzy C means; Fuzzy logic; Fuzzy set theory; Gaussian mixture modeling; Image segmentation; Internal Medicine; Investigative techniques, diagnostic techniques (general aspects); Mathematical models; Medical sciences; Memory organisation. Data processing; Nervous system; Other; Pattern recognition. Digital image processing. Computational geometry; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Segmentation; Software; Fuzzy C means; Image segmentation; Corpus collasum; Gaussian mixture modeling; Cluster analysis; Brain; Density estimation; Automatic classification; Similarity; Image processing; Gaussian distribution; Modeling; Center of mass; Diagnosis; Mixed distribution; Membership function; Decision making; Cluster; Statistical decision; Nuclear magnetic resonance imaging; Corpus callosum; Jaccard index; Fuzzy logic; Gaussian process; Planning
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2013.06.006

Abstract This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.