Tumor Segmentation From a Multispectral Mri Images by Using Support Vector Machine Classification

Ruan, Su; Lebonvallet, S.; Merabet, Adel; Constans, Jean-Marc

doi:10.1109/isbi.2007.357082

Cited by 51 publications

(37 citation statements)

References 8 publications

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“…From Tab.1, SVM-RFE and KCS can obtain superior performance, because they can be effectively integrated with SVM used in the segmentation step. Our old method [2] with SVM, but without feature selection, is worse than SVM-RFE and KCS which use a feature selection.…”

Section: Experimentationmentioning

confidence: 92%

“…Because of the large amount of data from multispectral medical images and great differences among different patients, this problem is still unsolved and many algorithms are proposed [1]. In our early work [2], a follow-up system of brain tumor was proposed. As we used all features extracted from all types of MRI modalities without any selection, the system not only was timeconsuming, but also led to imprecision segmentations due to the redundant and imprecise information.…”

Section: Introductionmentioning

confidence: 99%

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Image fusion for following-up brain tumor evolution

Ruan

Zhang

Liao

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination per four months): feature selection, SVMbased segmentation, and contour refinement. The training of the tumor is carried out only on the first MRI examination (before the treatment). Six feature selection methods are tested in our system. The quantitative comparisons of the methods' and the expert's manual traces demonstrate the effectiveness of the fusion by the feature selection. Results of following-up three patients over one year show that our system can provide a good tool to evaluate the therapeutic treatment.

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Section: Experimentationmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Image fusion for following-up brain tumor evolution

Ruan

Zhang

Liao

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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“…It is the heart of any system of vision which aims at extracting visual volumes over time. Multiple papers have been proposed to improve and accelerate it [12][13][14]. Volume measurements give absolute values and do not take into account the localization of anomalies in images.…”

Section: Classical Estimation Methodsmentioning

confidence: 99%

Towards a Novel Approach for Tumor Volume Quantification

Kharbach¹,

Bellach²,

Rahmoune³

et al. 2017

J. Imaging

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Abstract:In medical image processing, evaluating the variations of lesion volume plays a major role in many medical applications. It helps radiologists to follow-up with patients and examine the effects of therapy. Several approaches have been proposed to meet with medical expectations. The present work comes within this context. We present a new approach based on the local dissimilarity volume (LDV) that is a 3D representation of the local dissimilarity map (LDM). This map presents a useful means to compare two images, offering a localization of information. We proved the effectiveness of this method (LDV) compared to medical techniques used by radiologists. The result of simulations shows that we can quantify lesion volume by using the LDV method, which is an efficient way to calculate and localize the volume variation of anomalies. It allowed a time savings with the compete satisfaction of an expert during the medical treatment.

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“…Segmentation by weighted aggregation (SWA) is used to provide the multi-level segmentation of data. Ruan et al [12] proposed a supervised machine learning technique to track the tumor volume. The complete process is categorized into two main steps.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain tumor classification from multi-modality MRI using wavelets and machine learning

Khalid

Rajpoot

2017

Pattern Anal Applic

204

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In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multimodal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skullstripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions (complete tumor, active tumor and enhancing tumor). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge.

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Tumor Segmentation From a Multispectral Mri Images by Using Support Vector Machine Classification

Cited by 51 publications

References 8 publications

Image fusion for following-up brain tumor evolution

Image fusion for following-up brain tumor evolution

Towards a Novel Approach for Tumor Volume Quantification

Brain tumor classification from multi-modality MRI using wavelets and machine learning

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