A hybrid segmentation and classification techniques for detecting the neurodegenerative disorder from brain Magnetic Resonance Images

The mechanism of detecting the neurodegenerative disorder from Magnetic Resonance Images (MRIs) is one of the demanding and critical process in recent days. For this purpose, the existing works introduced some of the segmentation and classification techniques, which were used to detect the abnormal region from the brain images. However, it limits the problems of over segmentation, inefficient classification, and more complexity. The early predictions and the diagnosis process of neurodegenerative-disorders were accomplished by the use of segmentation and classification approaches of various methods. The proposed methodology focused on developing an integrated segmentation and classification techniques for an accurate brain disease classification. Here, the most extensively used segmentation techniques such Particle Swarm Optimization (PSO) and Self-Organizing Map (SOM) techniques are integrated for enabling an efficient image segmentation. In addition, it segments the Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF) regions. Consequently, the most suitable features are extracted from the segmented image by using the Neighbor Intensity Pattern (NIP) extraction technique. Based on these features, the normal and abnormal regions are classified by the use of an integrated Neural Network and K-Nearest Neighbor (KNN) classification techniques. The hybridization of the work is, that it integrates the benefits of various segmentation and classification techniques, which leads to increased detection efficiency and classification accuracy. The performance of these techniques are evaluated by using two different datasets such as ADNI and PPMI, which contains more number of brain MRIs. Also, various performance parameters have been utilized to test the results of the proposed system. Moreover, the traditional classification techniques are considered to compare the results of the proposed classification technique. During experimental evaluation, the performance of the techniques are validated by using different measures, and the results are compared with other existing techniques for analyzing the efficiency of proposed mechanism. At last, the results stated that the NN-KNN outperforms the other techniques by exactly locating the affected regions. The proposed framework exhibits the higher performance of accuracy level with 98.6%, sensitivity rate of 95%, exposed 96% of specificity rate and acquires the efficient precision rate of 99.21%. In future, this work can be expanded by using some advanced techniques for classifying other brain diseases.