Performance Evaluation of Fuzzy C Means Segmentation and Support Vector Machine Classification for MRI Brain Tumor

Srinivas, B.; Rao, G. Sasibhushana

doi:10.1007/978-981-13-1595-4_29

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…The feature extraction approaches [12, [138][139][140] including GLCM [15,141,142], geometrical features (area, perimeter, and circularity) [15], first-order statistical (FOS), GWT [143,144], Hu moment invariants (HMI) [145], multifractal features [146], 3D Haralick features [147], LBP [148], GWT [11], HOG [14, 137], texture and shape [82, 143,149,150], co-occurrence matrix, gradient, run-length matrix [151], SFTA, curvature features [152,153], Gabor like multiscale texton features [154], Gabor wavelet and statistical features [142,143] are utilized for classification. Table 3 lists the summary of feature extraction methods.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

210

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Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

210

View full text Add to dashboard Cite

show abstract

“…Applying Morphological operations highly reduces the computational time for tumor segmentation that has been proved (Devkota et al, 2017). (Mathew & Anto, 2017;Srinivas & Sasibhushana, 2019) are one of the best classifier under supervised learning technique. It is highly used in medical image classification with two types namely linear and non linear.…”

Section: A B S T Max a S X T Y B X Y S X T Y D S Y Dmentioning

confidence: 99%

Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

Sumathi¹,

Mandadi²

2021

International Journal of E-Health and Medical Communications

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The authors designed an automated framework to segment tumors with various image sequences like T1, T2, and post-processed MRI multimodal images. Contrast-limited adaptive histogram equalization method is used for preprocessing images to enhance the intensity level and view the tumor part clearly. With the combination of kernel possibilistic c means clustering with particle swarm optimization technique, a tumor part is segmented, and morphological filters are applied to remove the unrelated outlier pixels in the segmented image to detect the accurate tumor part. The authors collected various image sequences from online resources like Harvard brain dataset, BRATS, and RIDER, and a few from clinical datasets. Efficiency is ensured by computing various performance metrics like Jaccard Index MSE, PSNR, sensitivity, specificity, accuracy, and computational time. The proposed approach yields 97.06% segmentation accuracy and 98.08% classification accuracy for multimodal images with an average of 5s for all multimodal images.

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“…The validation of the presented approach is conducted on BRATS 2013 data set and showed improved performance. Srinivas and Rao () used Fuzzy C‐Means approach for tumor segmentation and extract their features like DWT. Later, a Principle Component Analysis (PCA) is performed for the reduction of irrelevant features and classified through SVM.…”

Section: Related Workmentioning

confidence: 99%

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

151

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Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

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Performance Evaluation of Fuzzy C Means Segmentation and Support Vector Machine Classification for MRI Brain Tumor

Cited by 17 publications

References 12 publications

Brain tumor detection and classification using machine learning: a comprehensive survey

Brain tumor detection and classification using machine learning: a comprehensive survey

Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

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