In texture analysis the common methods are based on the gray levels of the texture image. However, the use of color information improves the classification accuracy of the colored textures. In the classification of nonhomogenous natural textures, human texture and color perception are important. Therefore, the color space and texture analysis method should be selected to correspond to human vis...

A combined neural network and rule-based approach is suggested as a general framework for pattern recognition. This approach enables unsupervised and supervised learning, respectively, while providing probability estimates for the output classes. The probability maps are utilized for higher level analysis such as a feedback for smoothing over the output label maps and the identification of unkn...

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visua...

This chapter presents a comparative study of texture classification in computed tomography images of the human lungs. Popular texture descriptors used in the medical image analysis literature for texture-based emphysema classification are described and evaluated within the same classification framework. Further, it is investigated whether combining the different descriptors is beneficial.

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives...

The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most exi...