Journal of Pathology Informatics Journal of Pathology Informatics
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TECHNICAL NOTE
Year : 2016  |  Volume : 7  |  Issue : 1  |  Page : 36

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix


1 Department of Quantitative Pathology and Immunology; Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan
2 Chi.Co.LTD, Tokyo, Japan
3 Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA
4 Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan
5 Department of Pathology, Shinshu University School of Medicine, Nagano, Japan

Correspondence Address:
Yoichiro Yamamoto
Department of Pathology, Shinshu University School of Medicine, Nagano
Japan
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.189699

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Background: Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. Methods and Results: In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. Conclusion: CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.


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