Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

Abdullah, Bassem A.; Younis, Akmal A.; Pattany, Pradip M.; Saraf‐Lavi, Efrat

doi:10.4236/ojmi.2011.12005

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…SVMs perform classification by finding a maximum-margin hyperplane that separates the two classes while maximizing the distance between the nearest point from either class. SVMs have been widely used for lesion segmentation tasks in MS populations (Ferrari et al, 2003) (Abdullah et al, 2011) as well as for WMH segmentation in aging and AD populations (Ithapu et al, 2014) (Quddus et al., 2005).…”

Section: Methodsmentioning

confidence: 99%

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

et al. 2017

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Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

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Section: Methodsmentioning

confidence: 99%

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

et al. 2017

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“…Support vector machines (SVM) is a popular and widely used supervised learning algorithm and has been applied to the MS lesion segmentation problem [9,10]. The method extracts some features from examples of lesion and non-lesion voxels.…”

Section: Segmentation Methods: State Of the Artmentioning

confidence: 99%

Multiple sclerosis lesion detection with local multimodal Markovian analysis and cellular automata ‘GrowCut’

2014

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Multiple sclerosis affect over 2.5 million people world-wide. This autoimmune disease of the central nervous system causes damage to the insulating myelin sheaths around the axons in the brain. The disease progresses at different rates in different people and can have periods of remission and relapse. A fast and accurate method for evaluating the number and size of MS lesions in the brain is a key component in evaluating the progress of the disease and the efficacy of treatments. Manual segmentation is slow and difficult and the results can be somewhat subjective. It requires a physician to consider several MRI slices across multiple modalities. The power and speed of computer systems provide an obvious avenue to help. While many automated methods exist, they have not reached human-level accuracy of the segmentation results. There exists a need for a robust, fast and accurate method to improve the results of automatic MS lesion segmentation methods. We propose a post-processing stage to improve the segmentation results of an existing system. It uses two different strategies to improve the segmentation results of an automated system based on whole-brain tissue classification and lesion detection. The first strategy leverages the current processing system at a granularity finer than the whole brain to detect lesions at a local level. This reflects the way that a physician considers only a part of the brain at a time. It then combines the series of local results to produce a whole-brain segmentation. This approach better captures the local lesion properties and produces encouraging results, with a general improvement in the detection rate of lesions. The second method dives deeper and looks at the individual voxel level. Just as a physician might look more closely at a lesion, it considers the local neighborhood around a lesion detection. The method selects seed points from the existing results and uses a region growing method based on cellular automata. It grows the lesion areas based on a local neighborhood similarity in intensity. Over the eleven patients examined, some results improved over the base case and show the efficiency of the proposed approach.

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“…The output segmentations of the axial, sagittal and coronal sectional views' segmentation engines are aggregated to generate the final accurate segmentation. This work is an extension to our segmentation framework proposed for single view-single MRI channel [45].…”

Section: Discussionmentioning

confidence: 99%

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

Abdullah¹,

Younis²,

John³

2012

TOBEJ

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Abstract:In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

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Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

Cited by 13 publications

References 25 publications

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Multiple sclerosis lesion detection with local multimodal Markovian analysis and cellular automata ‘GrowCut’

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

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