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Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features

Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features

N. Hema Rajini, R. Bhavani
Copyright: © 2016 |Pages: 21
ISBN13: 9781466696853|ISBN10: 1466696850|EISBN13: 9781466696860
DOI: 10.4018/978-1-4666-9685-3.ch018
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MLA

Rajini, N. Hema, and R. Bhavani. "Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features." Emerging Technologies in Intelligent Applications for Image and Video Processing, edited by V. Santhi, et al., IGI Global, 2016, pp. 441-461. https://doi.org/10.4018/978-1-4666-9685-3.ch018

APA

Rajini, N. H. & Bhavani, R. (2016). Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features. In V. Santhi, D. Acharjya, & M. Ezhilarasan (Eds.), Emerging Technologies in Intelligent Applications for Image and Video Processing (pp. 441-461). IGI Global. https://doi.org/10.4018/978-1-4666-9685-3.ch018

Chicago

Rajini, N. Hema, and R. Bhavani. "Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features." In Emerging Technologies in Intelligent Applications for Image and Video Processing, edited by V. Santhi, D. P. Acharjya, and M. Ezhilarasan, 441-461. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9685-3.ch018

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Abstract

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 chapter presents a new approach to automated detection of ischemic stroke using k-means clustering technique which separates the lesion region from healthy tissues and classification of ischemic stroke using texture features. The proposed method has five stages, pre-processing, tracing midline of the brain, extraction of texture features and feature selection, classification and segmentation. In the first stage noise is suppressed using a median filtering and skull bone components of the images are removed. In the second stage, midline shift of the brain is calculated. In the third stage, fourteen texture features are extracted and optimal features are selected using genetic algorithm. In the fourth stage, support vector machine, artificial neural network and decision tree classifiers have been used. Finally, the ischemic stroke region is extracted by using k-means clustering technique.

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