An Approach Patch-Based for the Segmentation of Pathologies Application to Glioma Labelling

Authors(3) :-Syeda Meraj Bilfaqih, Sabahat Khatoon, Mariyam Ayesha Bivi

In this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tu-mors by using patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model auto-matically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing.

Authors and Affiliations

Syeda Meraj Bilfaqih
Department Of Computer Science, King Khalid University, Saudi Arabia
Sabahat Khatoon
Department Of Computer Science, King Khalid University, Saudi Arabia
Mariyam Ayesha Bivi
Department Of Computer Science, King Khalid University, Saudi Arabia

Patch-Based, Glioma, Segmentation.

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Publication Details

Published in : Volume 3 | Issue 2 | March-April 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 196-209
Manuscript Number : IJSRSET173273
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Syeda Meraj Bilfaqih, Sabahat Khatoon, Mariyam Ayesha Bivi, " An Approach Patch-Based for the Segmentation of Pathologies Application to Glioma Labelling, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 2, pp.196-209, March-April-2017.
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