Automated classification of magnetic resonance brain images using Wavelet Genetic Algorithm and Support Vector Machine

Kharrat, Ahmed; Messaoud, Mohamed Ben; Abid, Mohamed; Gasmi, Karim; Benamrane, Nacéra

doi:10.1109/coginf.2010.5599712

Cited by 71 publications

(93 citation statements)

References 10 publications

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“…Low-level feature extracted from images and high-level knowledge from specialists is combined into system. Ahmed Kharrat, Karim Gasmi, Mohamed Ben Messaoud [10], presented their work on A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and SVM. This paper proposes a genetic algorithm and SVM based classification of brain tumor.…”

Section: Literature Surveymentioning

confidence: 99%

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW <br/>—Metabolite Peak Height Scanning Method

Gonal¹,

Kohir²

2017

OJMI

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In this paper, we attempt to resolve the problem of grading of brain tumors as grade 2, grade 3, grade 4, using information from magnetic resonance spectroscopy (MRS) image, to assist in clinical diagnosis. This paper proposes a novel approach to extract metabolite values represented in a graphical form in MR Spectroscopy image. Metabolites like N-acetyl aspartate (NAA), Choline (CHO) along with the metabolite ratios NAA/CHO and presence/absence of LACTATE peak play the most important role in deciding the tumor type. The proposed approach consists of several steps including preprocessing, metabolite peak height scanning and classification. Proposed system stores the metabolite values in dataset instead of storing MRS images; so reduces the image processing tasks and memory requirements. Further these metabolite values and ratios are fed to a BPN classifier. Experimental results demonstrate the effectiveness of the proposed approach in classifying the brain tumors.

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Section: Literature Surveymentioning

confidence: 99%

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW <br/>—Metabolite Peak Height Scanning Method

Gonal¹,

Kohir²

2017

OJMI

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“…This was followed by the tumor area detection by using thresholding segmentation, and then by features extraction by using GMM. Kharrat, et al [8] and Beura, et al [9] used Grey Level Cooccurrence Matrix method (GLCM) and wavelet features to extract texture features in their studies. Kharrat, et al [8] developed an automated algorithm to classify the MRI brain tumors into normal, benign and malignant.…”

Section: Related Workmentioning

confidence: 99%

“…Kharrat, et al [8] and Beura, et al [9] used Grey Level Cooccurrence Matrix method (GLCM) and wavelet features to extract texture features in their studies. Kharrat, et al [8] developed an automated algorithm to classify the MRI brain tumors into normal, benign and malignant. Beura, et al [9] used these texture features to classify the breast tissues into normal, benign and malignant tumors by using mammogram images and the significance of the features were measured by using the F-statistic method.…”

Section: Related Workmentioning

confidence: 99%

MRI brain scan classification using novel 3-D statistical features

Hasan

Meziane

Aspin

et al. 2017

Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing

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The paper presents an automated algorithm for detecting and classifying magnetic resonance brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix approach that is used for extracting texture features from MRI brain scans. This approach is used to analyze and measure asymmetry between the two brain hemispheres, based on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.

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“…Normal and abnormal slices are differentiated by Selveraj et al [8] extracted five GLCM features from different offsets and two statistical features from the images. Kharrat et al [9] discriminated between normal and a malignant tumor by extracting 44 GLCM features. Zarchari et al [10] performed multiclass classification of brain tumors by extracting 100 features from GLCM, Gabor, intensity, shape, and statistical feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification

et al. 2013

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Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %.PCA-ANN has delivered high accuracy for each class: A S -9 0 . 7 4 % , G B M -8 8 . 4 6 % , M E D -8 5 % , MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

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Automated classification of magnetic resonance brain images using Wavelet Genetic Algorithm and Support Vector Machine

Cited by 71 publications

References 10 publications

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW <br/>—Metabolite Peak Height Scanning Method

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW <br/>—Metabolite Peak Height Scanning Method

MRI brain scan classification using novel 3-D statistical features

Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification

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Automated classification of magnetic resonance brain images using Wavelet Genetic Algorithm and Support Vector Machine

Cited by 71 publications

References 10 publications

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW &lt;br/&gt;—Metabolite Peak Height Scanning Method

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW &lt;br/&gt;—Metabolite Peak Height Scanning Method

MRI brain scan classification using novel 3-D statistical features

Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification

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Product

Resources

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Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW <br/>—Metabolite Peak Height Scanning Method

Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW <br/>—Metabolite Peak Height Scanning Method