Performance Evaluation of Kernel Based Techniques for Brain MRI Data Classification

Gohokar, V. V.; Gohokar, V.N.

doi:10.1109/iccima.2007.320

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Selvathi et al, [6] has developed the Texture features like gray level co-occurrence and statistics are extracted using MRI brain data. The Classification is based on advanced kernel based techniques such as Support Vector Machine (SVM) and the Relevance Vector Machine (RVM) is used for normal and abnormal are deployed.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using Mri Brain Tumor Image

A¹,

Balasubramanian²

2015

IJFCST

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Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to detect the MRI brain tumor images. There are two parts, namely; feature extraction process and classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of 1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform technique provides better than curvelet transform and Local ternary pattern.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using Mri Brain Tumor Image

A¹,

Balasubramanian²

2015

IJFCST

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“…This data set has been introduced by Blank et al 27 It consists of 93 uncompressed videos in avi format with low resolution (180 × 144 pixels, 25 frames∕s), including 10 different activities (the numbers in parentheses signify the respective number of videos): bending (9), jumping-jack (9), jumping forward on two legs (9), jumping in place on two legs (9), running (10), galloping sideways (9), skipping (10), walking (10), one-hand waving (9), and two-hands waving (9). The videos are colorful, and the actors are of both genders.…”

Section: The Weizmann Data Setmentioning

confidence: 99%

“…So far, many well-known pattern recognition techniques, such as k-nearest neighbor (k-NN), support vector machine (SVM), 4,9 and relevance vector machine (RVM), as well as their variants have been developed and applied in activity recognition. So far, many well-known pattern recognition techniques, such as k-nearest neighbor (k-NN), support vector machine (SVM), 4,9 and relevance vector machine (RVM), as well as their variants have been developed and applied in activity recognition.…”

Section: Introductionmentioning

confidence: 99%

Recognition of human activities using a multiclass relevance vector machine

Yow

Guo

2012

Opt. Eng

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We address the issue of human activity recognition by introducing the multiclass relevance vector machine (mRVM), the current state-of-the-art kernel machine learning technology given the multiclass classification problems (actually, activity recognition can commonly be viewed as a multiclass classification problem). Under our proposed recognition framework, the required procedure consists of three functional cascade modules: a. detecting the human silhouette blobs from the image sequence by the background subtraction method; b. extracting the shape and the motion features from the variation energy image (VEI); and c. sending the obtained features to the mRVM and recognizing the human activity. There are two types of mRVM: the constructive mRVM 1 and the top-down mRVM 2 . We performed 10 times three-fold crossvalidation on the Weizmann benchmark data set to examine the effectiveness of the proposed method. We also compared our method with other existing approaches, and the experimental results show that the proposed method offers superior performance. In summary, the mRVM, especially the mRVM 2 , has advantages both in terms of recognition rate and sparsity, along with a simple feature extraction process. The mRVM also significantly simplifies the classification process, by comparison with traditional binary-tree style multiclass classifiers. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).

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“…SVM achieves wide applications in many area attributing to its efficiency and rigorous mathematical background. Because of its efficiency and rigorous mathematical background, SVM achieves wide applications in various fields, such as microcalcification classification in breast cancer [ 26 – 29 ], text classification [ 25 , 27 – 29 ], and voice recognition [ 30 ].…”

Section: Lymph Nodal Classification Modelmentioning

confidence: 99%

A Novel Approach to Segment and Classify Regional Lymph Nodes on Computed Tomography Images

Cai

Cui

Tian

et al. 2012

Computational and Mathematical Methods in Medicine

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Morphology of lymph nodal metastasis is critical for diagnosis and prognosis of cancer patients. However, accurate prediction of lymph node type based on morphological information is rarely available due to lack of pathological validation. To obtain correct morphological information, lymph nodes must be segmented from computed tomography (CT) image accurately. In this paper we described a novel approach to segment and predict the status of lymph nodes from CT images and confirmed the diagnostic performance by clinical pathological results. We firstly removed noise and preserved edge details using a revised nonlinear diffusion equation, and secondly we used a repulsive-force-based snake method to segment the lymph nodes. Morphological measurements for the characterization of the node status were obtained from the segmented node image. These measurements were further selected to derive a highly representative set of node status, called feature vector. Finally, classical classification scheme based on support vector machine model was employed to simulate the prediction of nodal status. Experiments on real clinical rectal cancer data showed that the prediction performance with the proposed framework is highly consistent with pathological results. Therefore, this novel algorithm is promising for status prediction of lymph nodes.

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Performance Evaluation of Kernel Based Techniques for Brain MRI Data Classification

Cited by 11 publications

References 20 publications

Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using Mri Brain Tumor Image

Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using Mri Brain Tumor Image

Recognition of human activities using a multiclass relevance vector machine

A Novel Approach to Segment and Classify Regional Lymph Nodes on Computed Tomography Images

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