Abstrait

Computer Aided Detection Of Ischemic Stroke Using Cellular Automata

Teena Thomas, Jobin Jose

Computed tomography images are widely used in the diagnosis of ischemic stroke because of its faster acquisition and compatibility with most life support devices. This paper presents a new approach to automated detection of ischemic stroke using cellular automata, midline shift and image feature characteristics, which separate the ischemic stroke region from healthy tissues in computed tomography images. The proposed method consists of five stages namely, preprocessing, segmentation, tracing midline of the brain, extraction of texture features and classification. The application of the proposed method for early detection of ischemic stroke is demonstrated to improve efficiency and accuracy of clinical practice. The results are quantitatively evaluated by a human expert.. A classification with accuracy of 98%,has been obtained by SVM.