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Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA\u2010SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA\u2010SVM, can recognize the concentric structure and visualized features easily. 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