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Segmentation of Tissues in Brain MRI Images using Dynamic Neuro-Fuzzy Technique

Author(s): S.Javeed Hussain | T. Satya Savithri | P.V. Sree Devi

Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307

Volume: 1;
Issue: 6;
Start page: 416;
Date: 2012;
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Keywords: MRI | FFBNN | FIS

In this paper, an efficient technique is proposed for the precise segmentation of normal and pathological tissues in the MRI brain images. The proposed segmentation technique initially performs classification process by utilizing Fuzzy Inference System (FIS) and FFBNN. Both classifiers are utilizing the extracted image features as an input for the classification process. The features that are extracted in two ways from the MRI brain images. The FIS are used to make the classification process by generating the fuzzy rules using extracted features. Five features are extracted from the MRI images: they are two dynamic statistical features and three 2D wavelet decomposition features. In Segmentation, the normal tissues such as WM (White Matter), GM (Gray Matter) and CSF (Cerebrospinal Fluid) are segmented from the normal MRI images and pathological tissues such as Edema and Tumor are segmented from the abnormal images. The non-cortical tissues in the normal images are removed by the preprocessing stage. The implementation result shows the efficiency of proposed tissue segmentation technique in segmenting the tissues accurately from the MRI images. The performance of the segmentation technique is evaluated by performance measures such as accuracy, specificity and sensitivity. The performance of segmentation process is analyzed using a defined set of MRI brain image and compared against K-means clustering and Fuzzy ANN based segmentation methods.
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