Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine

Ahmad, Mubashir; Shafi, Imran; Osman, Abdelrahman

doi:10.9790/0661-0822531

Cited by 31 publications

(12 citation statements)

References 12 publications

(18 reference statements)

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“…In frequency domain the image is transformed to its frequency representation and various operations are performed. Gabor filter [13], Rotation invariant circular Gabor filter [13], Discrete Wavelet Transform [17,18], Cosine Transform are the methods used in spatial domain.…”

Section: Related Workmentioning

confidence: 99%

“…In [18] author used Dominant gray level run length matrix (DGLRLM), Wavelet based method (DWT) [17] and with Spatial Gray Level Dependence Matrix method (SGLDM) methods for classification using SVM. It was found that classification is best done by using dominant run length feature extraction method and an average accuracy rate of above 97 % was obtained…”

Section: Related Workmentioning

confidence: 99%

“…Several methods are used for Feature extraction, such as Gray Level co-occurrence Matrix(GLCM) [1][2][3][4][5][6][7][8][9][10], Dominant Gray Level Run Length Matrix method(DGLRLM) [13,18], Spatial Gray Level Dependence Matrix method (SGLDM) [13,18], Discrete-Wavelet transform(DWT) [17], Gabor Filters [13] and Local Binary Patterns (LBP) [13][14][15][16] and a considerable number of quantitative features namely Mean, Variance, Contrast, Correlation, Entropy, Energy, Homogeneity, Kurtosis, Inverse Difference Moment, Sum Average, Sum Entropy, Difference Entropy, Inertia [1,2] can be extracted from images using different methodologies in order to characterize different properties, and further can be used as the input of classifier to classify image pixels.…”

Section: Introductionmentioning

confidence: 99%

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CLOM: Counting label occurrence matrix for feature extraction in MR images

Nagpal

Vidyarthi

Mittal

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix (CLOM). CLOM is based on the counting label of the gray level intensity values of an image and used to extract the textural features of an image for image classification. Four different orientations are used in CLOM for extracting features based on anticipated algorithm. Proposed feature extraction mechanism is dynamic in nature and is used in any domain of image processing and machine learning. CLOM algorithm is compared with some feature extraction algorithms like GLCM (gray level co-occurrence matrix) and Run length Matrix when CLOM is tested in medical domain for classification of brain tumor images. The experimental result shows that the proposed algorithm gives better accuracy than past algorithms when tested with classifiers like KNN and BPNN.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CLOM: Counting label occurrence matrix for feature extraction in MR images

Nagpal

Vidyarthi

Mittal

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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“…They compared the tumor grading methodology by using both deep learning CNNs and feed forward back propagation NNs. Ahmad et al used SVM classification approach for detecting and segmenting the tumors in brain MRI images. The authors extracted wavelet features from the noise reduced source brain MRI image, and these extracted wavelet features are classified using SVM classifier in order to classify the test brain MRI image into either normal or abnormal images.…”

Section: Literature Surveymentioning

confidence: 99%

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

Rajagopal¹

2019

Int J Imaging Syst Tech

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The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non‐Glioma image based on the optimized set of features. Furthermore, energy‐based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.

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“…In this module, Texture feature is defined by using Gray Level Co-occurrence Matrix (GLCM) [11]. Grayscale image from the segmentation phase is obtained from the color image, and then the image co-occurrence matrix is generated.…”

Section: An Automated Mri Brain Image Classification For Tumor Detmentioning

confidence: 99%

An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier

Vaishnavee¹,

Amshakala²

2015

2015 IEEE International Conference on Engineering and Technology (ICETECH)

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In recent days, image processing is an interesting research field and mainly the medical image processing is increasingly challenging field to process various medical image types. It is widely used in diagnosis of disease such as brain tumor, Cancer, Diabetes etc. and brain tumor is one such dangerous disease and currently moreover 600,000 people have this type of disease. Image segmentation is an important technique highly used to extract the suspicious parts from medical images such as MRI, CT scan, and Mammography etc. With this motivation in this work, SOM clustering is proposed for MRI brain image segmentation. Before the segmentation the Histogram Equalization is utilized for feature extraction which will improve the segmentation accuracy. After the segmentation process, the feature extraction using Gray Level Co-occurrence Matrix is utilized which avoids the formation of misclustered regions. The Principle Component Analysis (PCA) method is used for the feature selection to improve the classifier accuracy. An effective classifier Proximal Support Vector Machines (PSVM) is used to automatically detect the tumor from MRI brain image. This method is faster and computationally more efficient than the existing method SVM. While the SOM clustering with Histogram Equalization is a fast procedure for the segmentation of the whole volume and provides a way to model tissue classes, the PSVM-GLCM-PCA approach is a more robust scheme under noisy or bad intensity normalization conditions which produces better results using high resolution images, outperforming the results provided by other algorithms in the state-of-the art, in terms of the average overlap metric.

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Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine

Cited by 31 publications

References 12 publications

CLOM: Counting label occurrence matrix for feature extraction in MR images

CLOM: Counting label occurrence matrix for feature extraction in MR images

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier

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