Techniques for Detection and Analysis of Tumours from Brain MRI Images: A Review

Shah, Saurabh; Chauhan, N. C.

doi:10.14738/jbemi.31.1696

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Many methods have been investigated for medical image analysis; promising results have been provided by computational intelligence and machine learning methods in medical image processing [10]. The problem of brain tumour segmentation from multimodal MRI scans is still a challenging task, although recently various advanced methods of automated segmentation have been proposed to solve this task.…”

Section: Related Workmentioning

confidence: 99%

Automated brain tumor segmentation on multi-modal MR image using SegNet

et al. 2019

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The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only timeconsuming but also prone to human error, and its performance depends on pathologists' experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3D data sets for four MRI modalities (Flair, T1, T1ce, and T2) for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation. Taking the combined feature as input, a decision tree (DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017 (BraTS 2017) challenge, we achieved F -measure scores of 0.85, 0.81, and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively.Experimental results demonstrate that using SegNet models with 3D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.

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Section: Related Workmentioning

confidence: 99%

Automated brain tumor segmentation on multi-modal MR image using SegNet

et al. 2019

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“…Brain tumor characteristics include iso-intensity (different brain tumor tissues may have same signals as that of tumor), hypointensity (image may have darker shades than actual tumor tissues), hyperintensity (high-intensity areas), tumor heterogeneity and tumor homogeneity [28]. Ambiguities due to surrounding regions may include different type of edema (swelling) like perilesional edema [29]. Tumor intensities and shapes vary from patient to patient and same gray scales may be found in different tumor types.…”

Section: Tumor Classification Phases Using Brain Imagesmentioning

confidence: 99%

“…Different imaging sequences have varying capabilities to separate different brain tissues [29]. Tumor grade could be obtained using contrast enhancement on MRI and is commonly used in clinical systems [8,[48][49][50][51].…”

Section: Brain Tumor Imaging and Dataset Acquisitionmentioning

confidence: 99%

Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

et al. 2017

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Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three subgroups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification. Keywords Human brain cancer diagnosis and analysis Á Magnetic resonance imaging Á Human brain tumor multiclassification & Tanzila Saba

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“…Various techniques have been proposed for segmentation and classification of brain lesions. 18 Support vector machine (SVM) is a supervised binary classifier, originally aimed at classifying data into 2 classes. Based on a training dataset, the algorithm finds the hyperplane that maximally separates between points.…”

mentioning

confidence: 99%

“…SVM has been widely used as a classifier of medical images and has proved advantageous over other algorithms of its kind. 18,19 The aim of this study was to classify lesion areas in patients with HGG using SVM into 4 distinct components: 1) enhancing tumor, 2) enhancing nontumor, 3) nonenhancing tumor, and 4) nonenhancing nontumor. Segmentation of lesion areas into tumor and nontumor components refines the RANO criteria and may improve therapy response MRI assessment.…”

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confidence: 99%

Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine

Blumenthal¹,

Artzi

Liberman

et al. 2017

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Current imaging assessment of high-grade brain tumors relies on the Response Assessment in NeuroOncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assessments may fail to reliably distinguish tumor and nontumor. This study aimed to classify enhancing and nonenhancing lesion areas into tumor-versus-nontumor components.

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Techniques for Detection and Analysis of Tumours from Brain MRI Images: A Review

Cited by 11 publications

References 29 publications

Automated brain tumor segmentation on multi-modal MR image using SegNet

Automated brain tumor segmentation on multi-modal MR image using SegNet

Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine

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