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28,31,32 Two-dimensional FFT analysis of SHG images of both breast and ovarian cancers provide the ability to discern a more organized fibrillar collagen structure in cancerous tissue. However, as a stand-alone method, the technique is unable to detect the slight differences that are often present in the different types of cancers. Texture analyses The use of texture analyses has also been investigated in the analysis of SHG images of breast and ovarian cancer. In general, texture considers how the morphology in one part of an image relates to that of its neighbors. The simplest form of texture analysis is the gray-level co-occurrence matrix (GLCM), first developed by Haralick.33 This approach determines the texture via measuring the gray level of pixels oriented selleck chemicals 0��, 45��, 90��, and 135�� from an individual pixel or group of pixels. The gray levels of these neighbors are recorded in a matrix, in which mathematical relationships such as the correlation function, entropy, homogeneity, and energy can be used to describe the texture of the image. For example, correlation is useful in detecting the regularity of a particular structure, homogeneity is the weighted sum of the pixel PI3K inhibitor values and provides a metric of similarity of the pixel with its neighbors, and entropy describes the randomness of regions relative to their neighbors. In terms of SHG image analysis in cancer, several studies have used GLCM correlation function analysis and found that normal tissues had a different correlation than abnormal tissues, which corresponded to distinct fibers and less fibrillar structure in normal and abnormal tissues, PRDX4 respectively.11,28,31,32 In a more detailed analysis, Watson et al successfully combined the GLCM and 2D FFT into an SVM to describe the tissue remodeling of a mouse model of ovarian cancer with ?80% sensitivity and specificity.30,34 Recently, textons, a common computer vision application, has been added to the list of morphology-based image analysis tools.35 Textons are defined as repetitive patterns with slowly varying local statistical properties that are found within an image and are determined by convolving a group of pixels with a filter set of various shapes and sizes to identify repeating features.36 Wen et al employed a machine learning algorithm including textons and k-means clustering to classify human high-grade, serous ovarian carcinoma from normal stroma with 97% accuracy.35 Note that this approach does not identify discrete features such as an individual fiber sizes or alignment. However, textons are more versatile than other image transforms (ie, 2D FFT, curvelets, and wavelets) as customizable, intensity-independent filters and may be specifically designed for a unique application.