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Proceedings Paper

Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background
Author(s): Mohammad Faizal Ahmad Fauzi; Hamza Numan Gokozan; Brad Elder; Vinay K. Puduvalli; Jose J. Otero; Metin N. Gurcan
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Paper Abstract

Brain cancer surgery requires intraoperative consultation by neuropathology to guide surgical decisions regarding the extent to which the tumor undergoes gross total resection. In this context, the differential diagnosis between glioblastoma and metastatic cancer is challenging as the decision must be made during surgery in a short time-frame (typically 30 minutes). We propose a method to classify glioblastoma versus metastatic cancer based on extracting textural features from the non-nuclei region of cytologic preparations. For glioblastoma, these regions of interest are filled with glial processes between the nuclei, which appear as anisotropic thin linear structures. For metastasis, these regions correspond to a more homogeneous appearance, thus suitable texture features can be extracted from these regions to distinguish between the two tissue types. In our work, we use the Discrete Wavelet Frames to characterize the underlying texture due to its multi-resolution capability in modeling underlying texture. The textural characterization is carried out in primarily the non-nuclei regions after nuclei regions are segmented by adapting our visually meaningful decomposition segmentation algorithm to this problem. k-nearest neighbor method was then used to classify the features into glioblastoma or metastasis cancer class. Experiment on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7% for glioblastoma, 87.5% for metastasis and 88.7% overall. Further studies are underway to incorporate nuclei region features into classification on an expanded dataset, as well as expanding the classification to more types of cancers.

Paper Details

Date Published: 20 March 2014
PDF: 9 pages
Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410F (20 March 2014); doi: 10.1117/12.2043814
Show Author Affiliations
Mohammad Faizal Ahmad Fauzi, The Ohio State Univ. (United States)
Multimedia Univ. (Malaysia)
Hamza Numan Gokozan, The Ohio State Univ. (United States)
Brad Elder, The Ohio State Univ. (United States)
Vinay K. Puduvalli, The Ohio State Univ. Wexner Medical Ctr. (United States)
Jose J. Otero, The Ohio State Univ. (United States)
Metin N. Gurcan, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 9041:
Medical Imaging 2014: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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