Daouia Azzouz ◽  
Smaine Mazouzi
2015 ◽  
Vol 72 (2) ◽  
Sapideh Yazdani ◽  
Rubiyah Yusof ◽  
Alireza Karimian ◽  
Amir Hossein Riazi

Automatic segmentation of brain images is a challenging problem due to the complex structure of brain images, as well as to the absence of anatomy models. Brain segmentation into white matter, gray matter, and cerebral spinal fluid, is an important stage for many problems, including the studies in 3-D visualizations for disease detection and surgical planning. In this paper we present a novel fully automated framework for tissue classification of brain in MR Images that is a combination of two techniques: GLCM and SVM, each of which has been customized for the problem of brain tissue segmentation such that the results are more robust than its individual components that is demonstrated through experiments.  The proposed framework has been validated on brainweb dataset of different modalities, with desirable performance in the presence of noise and bias field. To evaluate the performance of the proposed method the Kappa similarity index is computed. Our method achieves higher kappa index (91.5) compared with other methods currently in use. As an application, our method has been used for segmentation of MR images with promising results.

G. Sandhya ◽  
Kande Giri Babu ◽  
T. Satya Savithri

The automatic detection of brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) from the MR images of the brain using segmentation is of immense interest for the early detection and diagnosing various brain-related diseases. MR imaging technology is one of the best and most reliable ways of studying the brain. Segmentation of MR images is a challenging task due to various artifacts such as noise, intensity inhomogeneity, partial volume effects and elemental texture of the image. This work proposes a region based, efficient and modern energy minimization process called as Anisotropic Multiplicative Intrinsic Component Optimization (AMICO) for the brain image segmentation in the presence of noise and intensity inhomogeneity to separate different tissues. This algorithm uses an efficient Anisotropic diffusion filter to decrease the noise. The denoised image gets segmented after the correction of intensity inhomogeneity by the MICO algorithm. The algorithm decomposes the MR brain image as two multiplicative intrinsic components, called as the component of the true image which represents the physical properties of the brain tissue and the component of bias field that is related to intensity inhomogeneity. By optimizing the values of these two components using an efficient energy minimization technique, correction of intensity inhomogeneity and segmentation of the tissues can be achieved simultaneously. Performance evaluation and the comparison with some existing methods have validated the remarkable performance of AMICO in terms of efficiency of segmentation of brain images in the presence of noise and intensity inhomogeneity.

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