Identification of Gliomas from brain MRI through adaptive segmentation and run length of centralized patterns

Gupta, Nidhi; Bhatele, Pushpraj; Khanna, Pritee

doi:10.1016/j.jocs.2017.02.009

Cited by 47 publications

(20 citation statements)

References 21 publications

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“…This dataset is available at smir.ch/BRATS/start2013 for download. This dataset is used in various research studies [19][20][21][22]40].…”

Section: Results and Evaluationmentioning

confidence: 99%

“…Apart from the morphological features, textural features are also very important when it comes to the classification of Glioma [18][19][20][21][22]. As in MRI sequences, cells of brain offer a very powerful textural property.…”

Section: Proposed Ewbprl Methods For Texture Feature Extractionmentioning

confidence: 99%

“…In 2017, Zhan et al come up with an automated approach for the grading of Glioma with the help of fusion of intensity, volume and texture features along with KNN classifier to deliver accuracy of 87.59 on the BRATS 2015 dataset [20]. In 2018, Gupta et al [21] proposed a clinical decision support system in order to assist the radiologist. This system utilizes the fusion of MRI pulse sequences and then applies adaptive thresholding for effective segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Bhatele¹,

Bhadauria²

2020

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This paper presents an efficient and accurate automated system based on the hybrid XGBoost with Random forest (XGBRF) ensemble model in order to classify the Glioma (type of mostly diagnosed brain tumor) into low grade and high grade Glioma. In this approach initially global thresholding is employed on various MRI sequence and their fusion combinations in order to perform the accurate segmentation. Then uses a proposed Enhanced wavelet binary pattern run length matrix method (EWBPRL) for textural features extraction from the region of interest or segmented Glioma tumor region. This proposed feature extraction method is based on the Discrete wavelet transform (DWT), Local Binary pattern (LBP) and Gray level run length Matrix (GLRLM) methods to extract texture features from the segmented region. Some morphological features are also computed from the segmented region along the textural features. Finally both these extracted features are employed in order to train a hybrid XGBoost with Random forest ensemble model for the first time. The proposed automated system apart from accurately detecting and segmenting the tumors region from the fused MRI sequences, also tends to determine the grading of Glioma in terms of severity. The proposed system is evaluated on the large size balance local dataset and as well as on the popular global datasets like BRATS 2013 and BRATS 2015. This approach offers an encouraging accuracy of 99.25% on the local dataset with the fusion of T1C+T2+Flair MRI sequence as compare to 96.75% accuracy, which is achieved utilizing the fusion of T1+T1C+T2+Flair MRI sequence.

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“…This dataset is available at smir.ch/BRATS/start2013 for download. This dataset is used in various research studies [19][20][21][22]40].…”

Section: Results and Evaluationmentioning

confidence: 99%

Section: Proposed Ewbprl Methods For Texture Feature Extractionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Bhatele¹,

Bhadauria²

2020

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“…Thus, a deep learning model by Zhao et al 26 solved the issues in Reference 25, but the segmentation performance was affected due to the difference between the number of pixels and classes. Gupta et al 27 used an adaptive thresholding for identifying the gliomas, which offered poor performance in noisy situations and required prior knowledge regarding segmentation. Additionally, DCNN for segmentation was applied by Hussain et al 28 , which rendered poor performance in the whole tumor region.…”

Section: Review Of the Literaturementioning

confidence: 99%

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Mahesh

Renjit²

2019

Int J Imaging Syst Tech

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Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer‐deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.

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“…Since brain tissues have complex structures, the intensity features are not sufficient for accurate segmentation of tumour, we use texture features to improve the accuracy of tumor segmentation. Gupta et al proposed a combined strategy called Run Length of Centralized Patterns (RLCP) [ 33 ]. In this method, local binary pattern (LBP) [ 34 ] code is indexed and gray level run length (GLRL) [ 35 ] matrix in principal directions are formed to count occurrences of runs length for each gray level.…”

Section: Image Segmentation Based On Graphical Modelsmentioning

confidence: 99%

Automated glioma detection and segmentation using graphical models

et al. 2018

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Glioma detection and segmentation is a challenging task for radiologists and clinicians. The research reported in this paper seeks to develop a better clinical decision support algorithm for clinicians diagnosis. This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI). A framework is constructed to learn structure of undirected graphical models that can represent the spatial relationships among variables and apply it to glioma segmentation. Compared with the pixel of image, the superpixel is more consistent with human visual cognition and contains less redundancy, thus, the superpixels are considered as the basic unit of structure learning and glioma segmentation scheme. ℓ1-regularization techniques are applied to learn the appropriate structure for modeling graphical models. Conditional Random Fields (CRF) are used to model the spatial interactions among image superpixel regions and their measurements. A number of features including statistics features, the combined features from the local binary pattern as well as gray level run length, curve features, and fractal features were extracted from each superpixel. The features are then passed by ℓ1-regularization to ensure a robust classification. The proposed method is compared with support vector machine and Fuzzy c-means to classify each superpixel into normal and abnormal tissue. The proposed system is tested for the presence of low grade as well as high grade glioma tumors on images collected from BRATS2013, BRATS2015 data set and Henan Provincial People’s Hospital (HNPPH) data set. The experiments performed provides similarity between segmented and truth image up to 91.5% by correlation method.

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Identification of Gliomas from brain MRI through adaptive segmentation and run length of centralized patterns

Cited by 47 publications

References 21 publications

Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Automated glioma detection and segmentation using graphical models

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