A taxonomy for texture description and identification
A taxonomy for texture description and identification
Image Processing: The Fundamentals
Image Processing: The Fundamentals
Colorectal Polyps Detection Using Texture Features and Support Vector Machine
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
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Texture is a powerful method to describe the appearance of different biological objects in images. The most used texture descriptor is the wellknown 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.