Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background

Mohammad Faizal Ahmad Fauzi; Hamza Numan Gokozan; Brad Elder; Vinay K. Puduvalli; Jose J. Otero; Metin N. Gurcan

doi:10.1117/12.2043814

20 March 2014 Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background

Mohammad Faizal Ahmad Fauzi, Hamza Numan Gokozan, Brad Elder, Vinay K. Puduvalli, Jose J. Otero, Metin N. Gurcan

Author Affiliations +

Proceedings Volume 9041, Medical Imaging 2014: Digital Pathology; 90410F (2014) https://doi.org/10.1117/12.2043814
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

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Mohammad Faizal Ahmad Fauzi, Hamza Numan Gokozan, Brad Elder, Vinay K. Puduvalli, Jose J. Otero, and Metin N. Gurcan "Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410F (20 March 2014); https://doi.org/10.1117/12.2043814

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