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histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第17期41卷 7820-7836页

作者： Nabizadeh, Nooshin John, Nigel Wright, Clinton Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ Miami Miller Sch Med Evelyn F McKnight Brain Inst Miami FL 33136 USA

Magnetic resonance imaging (MRI) is a very effective medical imaging technique for the clinical diagnosis and monitoring of heurological disorders. Because of intensity similarities between brain lesions and normal tissues, multispectral MRI modalities are usually applied for brain lesion detection. However, the time and cost restrictions for collecting multi-spectral MRI, and the issue of possible errors from registering multiple MR images necessitate developing an automatic lesion detection approach that can detect lesions using a single anatomical MRI modality. In this paper, an automatic algorithm for brain stroke and tumor lesion detection and segmentation using single-spectral MRI is presented. The proposed algorithm, called histogram-based gravitational optimization algorithm (HGOA), is a novel intensity-based segmentation technique, which applies enhanced gravitational optimization algorithm on histogram analysis results. The mathematical descriptions as well as the convergence criteria of the developed optimization algorithm are presented in detail. Using this algorithm, brain is segmented into different number of regions, which will be labeled as lesion or healthy. Here, the ischemic stroke lesions and tumor lesions are segmented with 91.5% and 88.1% accuracy, respectively. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Brain lesion segmentation Brain lesion detection histogram-based gravitational optimization algorithm MR imaging

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Automatic Tumor Lesion Detection and Segmentation Using histogram-based gravitational optimization algorithm

Automatic Tumor Lesion Detection and Segmentation Using Hist...

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IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

作者： Nabizadeh, Nooshin Dorodchi, Mohsen Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA

ISBN: (纸本)9781479945030

In this paper, an automated and customized brain tumor segmentation method is presented and validated against ground truth applying simulated T1-weighted magnetic resonance images in 25 subjects. A new intensity-based segmentation technique called histogram based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. While the mathematical foundation of this algorithm is presented in details, the application of the proposed algorithm in the segmentation of single T1-weighted images (T1-w) modality of healthy and lesion MR images is also presented. The results show that the tumor lesion is segmented from the detected lesion slice with 89.6% accuracy.

关键词： brain segmentation tumor detection magnetic resonance image histogram-based gravitational optimization algorithm

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