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.
Syeda Meraj Bilfaqih, Sabahat Khatoon, Mariyam Ayesha Bivi
Patch-Based, Glioma, Segmentation.
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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.
URL : http://ijsrset.com/IJSRSET173273.php