Affiliations: Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Tokyo, Japan | Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Tokyo, Japan | Global Scientific Information and Computing Center, Tokyo Institute of Technology, Tokyo, Japan | Department of Pathology, School of Medicine, Keio University, Keio, Japan
Abstract: Background: Histologic image analysis plays an important role in cancer diagnosis. It describes the structure of the body tissues and abnormal structure gives the suspicion of the cancer or some other diseases. Observing the structural changes of these chaotic textures from the human eye is challenging process. However, the challenge can be defeat by forming mathematical descriptor to represent the histologic texture and classify the structural changes via a sophisticated computational method. Objective: In this paper, we propose a texture descriptor to observe the histologic texture into highly discriminative feature space. Method: Fractal dimension describes the self-similar structures in different and more accurate manner than topological dimension. Further, the fractal phenomenon has been extended to natural structures (images) as multifractal dimension. We exploited the multifractal analysis to represent the histologic texture, which derive more discriminative feature space for classification. Results: We utilized a set of histologic images (belongs to liver and prostate specimens) to assess the discriminative power of the multifractal features. The experiment was organized to classify the given histologic texture as cancer and non-cancer. The results show the discrimination capability of multifractal features by achieving approximately 95% of correct classification rate. Conclusion: Multifractal features are more effective to describe the histologic texture. The proposed feature descriptor showed high classification rate for both liver and prostate data sample datasets.
