Liver fibrosis accurate staging is vital to define the state of the Schistosomiasis disease for further treatment. The present work analyzed the microscopic liver images to identify and to differentiate between healthy, cellular, fibrocellular, and fibrous liver pathologies by proposing a fast, robust, and highly discriminative method based on texture analysis. The multiclass classification based on the "one-versus- all" method that built a voting rule approach to classify the liver images based on the liver state. Specifically, quantitative parameters, such as the anisotropy and laminarity are proposed based on the relative orientation of the pixel pairs in a global and local coherence of gradient vectors approach. Analysis of the tissue texture data using both gradient vector and gradient angle co-occurrence matrix approaches facilitated more definitive identification of the abnormal tissue. The experimental results established that the local anisotropy based texture measures are appropriated for the microtexture analysis in order to discriminate between pathologies. Macrotexture description using the global features provided only integral anisotropy coefficient that has a confidence level similar to those provided by the local feature.
Keywords: 2D anisotropy histogram; Anisotropy; granulomas; laminarity; liver fibrosis; microscopic liver images; voting rule.
© 2017 Wiley Periodicals, Inc.