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Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

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Abstract

Follicular lymphoma (FL) is a cancer of lymph system and it is the second most common lymphoid malignancy in the western world. Currently, the risk stratification of FL relies on histological grading method, where pathologists evaluate hematoxilin and eosin (H&E) stained tissue sections under a microscope as recommended by the World Health Organization. This manual method requires intensive labor in nature. Due to the sampling bias, it also suffers from inter- and intra-reader variability and poor reproducibility. We are developing a computer-assisted system to provide quantitative assessment of FL images for more consistent evaluation of FL. In this study, we proposed a statistical framework to classify FL images based on their histological grades. We introduced model-based intermediate representation (MBIR) of cytological components that enables higher level semantic description of tissue characteristics. Moreover, we introduced a novel color-texture analysis approach that combines the MBIR with low level texture features, which capture tissue characteristics at pixel level. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the accuracy of the system significantly. The implemented system can identify the most aggressive FL (grade III) with 98.9% sensitivity and 98.7% specificity and the overall classification accuracy of the system is 85.5%.

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Acknowledgements

This work is supported in part by the US National Science Foundation (#CNS-0643969, #CNS-0403342, #CNS-0615155, #CCF-0342615), by the NIH NIBIB BISTI (#P20EB000591), NCI caBIG core middleware development (79077CBS10). The authors would like to thank Dr. Arwa Sha’naah, Dr. Amy Gewirtz, Dr. Frederick Racke, and Dr. John Zhao of The Ohio State University, Department of Pathology for providing the ground truth information and guidance, Dr. Pierluigi Porcu for useful discussions and Drs. Michael Pennell and Soledad Fernandez for the statistical design of the study.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH, 43210, USA

    Olcay Sertel, Jun Kong & Umit V. Catalyurek

  2. Department of Biomedical Informatics, The Ohio State University, 333 W Tenth Ave, Columbus, OH, 43210, USA

    Olcay Sertel, Jun Kong, Umit V. Catalyurek, Joel H. Saltz & Metin N. Gurcan

  3. Department of Pathology, The Ohio State University, 1645 Neil Ave, Columbus, OH, 43210, USA

    Gerard Lozanski

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  1. Olcay Sertel
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  2. Jun Kong
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  3. Umit V. Catalyurek
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  4. Gerard Lozanski
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  5. Joel H. Saltz
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  6. Metin N. Gurcan
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Correspondence to Olcay Sertel.

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Sertel, O., Kong, J., Catalyurek, U.V. et al. Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading. J Sign Process Syst Sign Image Video Technol 55, 169–183 (2009). https://doi.org/10.1007/s11265-008-0201-y

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