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

Abdullah, Bassem A.; Younis, Akmal A.; John, Nigel

doi:10.2174/1874230001206010056

Cited by 14 publications

(6 citation statements)

References 33 publications

(13 reference statements)

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“…In general, existing automatic lesion detection methods can be divided into two categories: (1) unsupervised methods based on prior knowledge [ 9 – 21 ]; (2) supervised methods based on machine learning [ 7 , 22 – 29 ].…”

Section: Introductionmentioning

confidence: 99%

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

et al. 2015

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BackgroundAccurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods.MethodsFirst, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection.ResultsWe tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al.ConclusionsIn this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a supervised component. In the unsupervised component, we proposed a method to identify lesioned hemisphere to help normalize the patient MRI with lesions and initialize/refine a lesion probability map. In the supervised component, we extracted three different-order statistical features from both the tissue/lesion probability maps obtained from the unsupervised component and the original MRI intensity. Three support vector machine classifiers are then trained for the three features respectively and combined for final voxel-based lesion classification.

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Section: Introductionmentioning

confidence: 99%

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

et al. 2015

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“…Manual segmentation and quantification of white matter lesion is time consuming, expensive, subjective and impractical for large scale longitudinal studies. Several supervised and unsupervised algorithms are proposed in the literature for the automatic segmentation of white matter lesions [8,9,10,11,12,13,9]. Among the many existing segmentation methods, the accurate segmentation of white matter lesions remains a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from intensity information, anatomical and textural information are also used to segment white matter lesions (WMLs) [8,10,11,12,13]. Geremia et al [17] proposed a method where a random forest classifier [18] is used to segment WMLs.…”

Section: Introductionmentioning

confidence: 99%

Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field

Roy

Bhuiyan

Janke

et al. 2015

Computerized Medical Imaging and Graphics

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White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.

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“…Two recent reviews 7, 8 discuss the most recent methods. Special mention deserve Garcia-Lorenzo et al, 9 who employ multimodal graph cuts, and Abdullah et al, 10 who train a SVM classifier with textual features extracted from multi-spectral MRI volumes.…”

Section: Introductionmentioning

confidence: 99%

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

et al. 2014

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Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

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

Cited by 14 publications

References 33 publications

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

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