Glioma grading using cell nuclei morphologic features in digital pathology images

Syed M. S. Reza; Khan M. Iftekharuddin

doi:10.1117/12.2217559

24 March 2016 Glioma grading using cell nuclei morphologic features in digital pathology images

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97852U (2016) https://doi.org/10.1117/12.2217559
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients’ images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Citation Download Citation

Syed M. S. Reza and Khan M. Iftekharuddin "Glioma grading using cell nuclei morphologic features in digital pathology images", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852U (24 March 2016); https://doi.org/10.1117/12.2217559

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