Abstract
Schizophrenia (SZ) is a brain disorder characterised by disturbances in cognition and emotional responsiveness. In this work, steered Hermite Transform (HT) and SVM are used to analyse SZ. The non-parametric region-based active contour method is used to skull strip the MR images obtained from NAMIC database. These images are subjected to steered HT, and features such as mean, energy (E0-E3), entropy and homogeneity are obtained. The significant features are selected using maximum relevance and subjected to classification. Results show that the proposed method is able to segment the brain region with higher accuracy (0.98), sensitivity (0.96) and F-score (0.95) compared to conventional methods. The prominent features mean and energy (E0) obtained from HT along with SVM could classify the normal and SZ better with an accuracy of 93.33% compared to Naive Bayes and K-nearest neighbour classifiers. Hence, this framework could be used for better diagnosis of Schizophrenia.
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