Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Engineering Applications of Artificial Intelligence
A new method based on the CLM of the LV RNN for brain MR image segmentation
Digital Signal Processing
Fuzzy c-means clustering with weighted image patch for image segmentation
Applied Soft Computing
Image segmentation based on histogram analysis utilizing the cloud model
Computers & Mathematics with Applications
IEEE Transactions on Information Technology in Biomedicine
Strong fuzzy c-means in medical image data analysis
Journal of Systems and Software
Generalized rough fuzzy c-means algorithm for brain MR image segmentation
Computer Methods and Programs in Biomedicine
Journal of Visual Communication and Image Representation
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
Segmentation of brain tissues using a 3-D multi-layer Hidden Markov Model
Computers in Biology and Medicine
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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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.