Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information

Capelle, Anne-Sophie; Colot, Olivier; Fernández-Maloigne, Christine

doi:10.1016/j.inffus.2003.10.001

Cited by 65 publications

(41 citation statements)

References 32 publications

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“…The 3D expectation-maximization method is extended using Hidden Markov models to infer tumor classification based on previous and subsequent segmentation results. (Capelle et al, 2004) proposed a segmentation method based on evidence theory for brain tumors on MRI. This method takes into account the spatial dependency between the voxels through an evidential spatial merging process.…”

Section: Markov Random Fields Image Segmentationmentioning

confidence: 99%

Brain Tumors: How Can Images and Segmentation Techniques Help?

Veloz¹,

Orellana²,

Vielma³

et al. 2011

Diagnostic Techniques and Surgical Management of Brain Tumors

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Section: Markov Random Fields Image Segmentationmentioning

confidence: 99%

Brain Tumors: How Can Images and Segmentation Techniques Help?

Veloz¹,

Orellana²,

Vielma³

et al. 2011

Diagnostic Techniques and Surgical Management of Brain Tumors

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“…The existing methods are divided into region-based and contour-based methods. Region-based methods [1][2][3][4][5][6][7][8][9] seek out clusters of pixels that share some measure of similarity. These methods reduce operator interaction by automating some aspects of applying the low level operations, such as threshold selection, histogram analysis, classification, etc.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Rajendran¹,

Dhanasekaran²

2014

INT J COMPUT COMMUN

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Deformable or snake models are extensively used for medical image segmentation, particularly to locate tumor boundaries in brain tumor MRI images. Problems associated with initialization and poor convergence to boundary concavities, however, has limited their usefulness. As result of that they tend to be attracted towards wrong image features. In this paper, we propose a method that combine region based fuzzy clustering called Enhanced Possibilistic Fuzzy C-Means (EPFCM) and Gradient vector flow (GVF) snake model for segmenting tumor region on MRI images. Region based fuzzy clustering is used for initial segmentation of tumor then result of this is used to provide initial contour for GVF snake model, which then determines the final contour for exact tumor boundary for final segmentation. The evaluation result with tumor MRI images shows that our method is more accurate and robust for brain tumor segmentation.

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“…The current methods usually rely also on T1-weighted contrast enhanced images [16]. This is the image we are trying to avoid, since it requires a contrast enhanced agent (usu-ally gadolinium) to be injected into the patient's blood, which breaks the non-invasivity of magnetic resonance.…”

Section: Introduction S Ince the Mr Technique Is Becoming More Populamentioning

confidence: 99%

Unsupervised Pathological Area Extraction using 3D T2 and FLAIR MR Images

Dvořák

Bartušek

Smékal

2014

Measurement Science Review

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This work discusses fully automated extraction of brain tumor and edema in 3D MR volumes. The goal of this work is the extraction of the whole pathological area using such an algorithm that does not require a human intervention. For the good visibility of these kinds of tissues both T2-weighted and FLAIR images were used. The proposed method was tested on 80 MR volumes of publicly available BRATS database, which contains high and low grade gliomas, both real and simulated. The performance was evaluated by the Dice coefficient, where the results were differentiated between high and low grade and real and simulated gliomas. The method reached promising results for all of the combinations of images: real high grade (0.73 ± 0.20), real low grade (0.81 ± 0.06), simulated high grade (0.81 ± 0.14), and simulated low grade (0.81 ± 0.04).

show abstract

Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information

Cited by 65 publications

References 32 publications

Brain Tumors: How Can Images and Segmentation Techniques Help?

Brain Tumors: How Can Images and Segmentation Techniques Help?

Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Unsupervised Pathological Area Extraction using 3D T2 and FLAIR MR Images

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