Brain Tumor Segmentation from Multispectral MRIs Using Sparse Representation Classification and Markov Random Field Regularization

Zhan, Tianming; Gu, Shenghua; Feng, Chao; Zhan, Yongzhao; Wang, Jin

doi:10.14257/ijsip.2015.8.9.24

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…After the feature extraction, the classification model is used. Frequently used classification patterns include fuzzy C-means (FCM) [14], Gaussian mixture model (GMM) [15], K-nearest neighbors (KNN) [16], support vector machine (SVM) [17][18][19], random forest (RF) [20], sparse representation-based classifier [21], and so on.…”

Section: S378mentioning

confidence: 99%

An automatic glioma grading method based on multi-feature extraction and fusion

Zhan

Feng

Hong³

et al. 2017

THC

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Section: S378mentioning

confidence: 99%

An automatic glioma grading method based on multi-feature extraction and fusion

Zhan

Feng

Hong³

et al. 2017

THC

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“…For the evaluation of their method total of 255 MRI images were used. Zhan et al [5] develop a method utilizing the intensity feature of multispectral MRI from both normal and abnormal. The feature is then passed to sparse representation classifier and also to Markov Random Field (MRF) regularization to classify into the tumor and normal tissues of the brain.…”

Section: Literaturementioning

confidence: 99%

Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Nida-Ur-Rehman¹,

Ahmed²,

Masood³

et al. 2017

ijacsa

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“…Tianming Zhan et al [9] made use of fully automatic method in order to obtain the segmentation of tumor from multispectral MR images of the brain. They used different intensity patches in the input image for representing the abnormal and normal tissues.…”

Section: Introduction Various Medicalmentioning

confidence: 99%

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

Virupakshappa¹,

Veerashetty²,

Ambika³

2022

SCPE

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The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.

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Brain Tumor Segmentation from Multispectral MRIs Using Sparse Representation Classification and Markov Random Field Regularization

Abstract: Abstract

Cited by 4 publications

References 20 publications

An automatic glioma grading method based on multi-feature extraction and fusion

An automatic glioma grading method based on multi-feature extraction and fusion

Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

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