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Texture Analysis in CT Brain Images Using a Reduced Run-Length Method
Abstract
In this paper a new method for texture classification of CT scan brain images based on Gray Level Run Length Method (GLRLM) is proposed. Other two conventional methods, Spatial Gray Level Dependency Method (SGLDM), and standard Gray Level Run-Length Method (GLRLM) are used to compare the performance of the proposed method. The feature vector consists of 14 Haralick features. The proposed algorithm applied to real time CT scan images. We achieved the classification rate 88% in the distinction between normal and abnormal images. Based on our experiments, the Reduced Gray Level Run Length Method (RGLRLM) is more appropriate than other methods for texture classification as it leads to higher classification accuracy.
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References
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