A Comparison between Haralick´s Texture Descriptor and the Texture Descriptor Based on Random Sets for Biological Images
Texture is a powerful method to describe the appearance of different biological objects in images. The most used texture descriptor is the well-known Haralick´s texture descriptor. We propose a texture descriptor based on random sets. This descriptor gives us more freedom in describing different textures. In this paper we compare the two texture descriptors based on a medical data set. We review the theory of the two texture descriptors and describe the procedure for the comparison of the two methods. A medical data set is used that is derived from colon examination. Decision tree induction is used to learn a classifier model. Cross-validation is used to calculate the error rate. The comparison of the two texture descriptors is based on the error rate, the properties of the two best classification models, the runtime for the feature calculation, the selected features, and the semantic meaning of the texture descriptors.
KeywordsGray Level Texture Feature Class Image Semantic Meaning Boolean Model
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