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


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.


CONCLUSIONS
CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.
Department of Quantitative Pathology and Immunology, Tokyo Medical University, Tokyo, Japan; Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan.
Akira
A
Saito
Chi.Co.LTD, Tokyo, Japan.
Yasushi
Y
Numata
Chi.Co.LTD, Tokyo, Japan.
Takuya
T
Hamada
Chi.Co.LTD, Tokyo, Japan.
Tomoyoshi
T
Horisawa
Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA.
Eric
E
Cosatto
Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA.
Hans-Peter
HP
Graf
Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan.
Masahiko
M
Kuroda
Department of Pathology, Shinshu University School of Medicine, Nagano, Japan.
Yoichiro
Y
Yamamoto
Cancer; heterogeneity; pleomorphism; quantification
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