Departmental Papers (BE)

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Conference Paper

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In this paper, multi-modal Magnetic Resonance (MR) images are integrated into a tissue profile that aims at differentiating tumor components, edema and normal tissue. This is achieved by a tissue classification technique that learns the appearance models of different tissue types based on training samples identified by an expert and assigns tissue labels to each voxel. These tissue classifiers produce probabilistic tissue maps reflecting imaging characteristics of tumors and surrounding tissues that may be employed to aid in diagnosis, tumor boundary delineation, surgery and treatment planning. The main contributions of this work are: 1) conventional structural MR modalities are combined with diffusion tensor imaging data to create an integrated multimodality profile for brain tumors, and 2) in addition to the tumor components of enhancing and non-enhancing tumor types, edema is also characterized as a separate class in our framework. Classification performance is tested on 22 diverse tumor cases using cross-validation.


Hongmin Cai, Ragini Verma, Yangming Ou, Seung-koo Lee, Elias R. Melhem, Christos Davatzikos: "Probabilistic Segmentation of Brain Tumors Based on Multi-Modality Magnetic Resonance Images". IEEE International Symposium on Biomedical Imaging (ISBI) 2007: pp. 600-603.


Brain tumors, tissue classification, multimodal MRI data, edema.



Date Posted: 20 August 2009

This document has been peer reviewed.