The Brain plays an imperative role in the life of human being as it manages the communication between sensory organs and muscles. Consequently, any disease related to brain should be detected at an early stage. Abundant accumulation of cerebrospinal fluid in the ventricle results to a brain disorder termed as normal pressure hydrocephalus (NPH). The current study aims to segment the ventricular part from CT brain scans and then perform classification to differentiate between the normal brain and affected brain having NPH. In the proposed method, firstly few preprocessing steps have been carried out to enhance the quality of the input CT brain image and ventricle region is cropped out. Then active contour model is employed to perform segmentation of the ventricle. Features are extracted from the segmented region and Ensemble classifier is used to classify CT brain scan into two classes namely, normal and NPH. More than hundreds of CT brain scans were analyzed during this study; area of ventricle has been used as a measure of feature extraction. Experimental results disclosed a significant improvement in case of ensemble classifier in comparison to Support Vector Machine in terms of its performance. © 2020, Springer Nature Singapore Pte Ltd.