Morphological Analysis of Cerebral Ventricles using MRI Images with Fuzzy Clustering Optimized by Harris-Hawk Optimization (HHO) and Aggregate Channel Features (ACF)

The morphology of the ventricles is used in studies of hydrocephalus, schizophrenia, tumors, trauma, Alzheimer's disease, Parkinson's disease, aging and atrophy to diagnose neurological diseases such as stroke, dementia and Huntington's disease. In this study, an automatic method for morphometric analysis of cerebral ventricles is proposed. Segmentation of cerebral ventricles is an important step to detect the landmarks of cerebral ventricles. Furthermore, the initial estimation of the cerebro-ventricular area can be effective in the proper segmentation of the ventricles. In this regard, Fuzzy C-Means Clustering (FCM) optimized with Harris-Hawk Optimization (HHO) and Aggregate Channel Features (ACF) are used. In order to measure the linear indices of cerebral ventricles, including Evans Index, Bicaudate Ratio, Bicaudate-Frontal Index, Bicaudate-Temporal Index, and Huckman Number, locating a number of landmarks on MRI images is required. This process is based on the geometric features of cerebral ventricles and the use of Hough transformation. The implementation results demonstrate that the proposed algorithm has the best performance with precision 90%, sensitivity 82%, specificity 99%, peak signal-to-noise ratio 77/11, dice similarity coefficient 86%, Jaccard index 75% and contour matching score 92% in the segmentation of cerebral ventricles among other compared methods. Additionally, the results show that the measurement accuracy of the proposed algorithm in the mentioned morphometric linear indices is 97%, 74%, 77%, 76% and 92% respectively.