Severity Analysis of Brain Tumor in MRI Images Using Modified Multi-texton Structure Descriptor and Kernel-SVM

Jayachandran, A.; Dhanasekaran, R.

doi:10.1007/s13369-014-1334-x

Cited by 35 publications

(15 citation statements)

References 18 publications

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“…HKSVM) [24]. The overall classification accuracy of the proposed method is 96.80 %, modified MTH with HKSVM is 94.32 %, and MTH with SVM is 91.13 %.…”

Section: Resultsmentioning

confidence: 92%

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

Jayachandran

Sundararaj

2015

Int. J. Fuzzy Syst.

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Image classification is one of the typical computational applications widely used in the medical field, especially for abnormality detection in magnetic resonance (MR) brain images. Medical image classification is a pattern recognition technique in which different images are categorized into several groups based on some similarity measures. One of the significant applications is the tumor type identification in abnormal MR brain images. The proposed multi-class brain tumor classification system comprises feature extraction and classification. In feature extraction, the attributes of the co-occurrence matrix and the histogram are represented within the feature vector. In this work, the advantage of both co-occurrence matrix and histogram to extract the texture feature from every segment is used for better classification. In classification, the fuzzy logic-based hybrid kernel is designed and applied to train the support vector machine for automatic classification of four different types of brain tumors such as Meningioma, Glioma, Astrocytoma, and Metastases. Based on the experimental results, the proposed brain tumor classification method is more robust than other traditional methods in terms of the evaluation metrics, sensitivity, specificity, and accuracy.

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“…HKSVM) [24]. The overall classification accuracy of the proposed method is 96.80 %, modified MTH with HKSVM is 94.32 %, and MTH with SVM is 91.13 %.…”

Section: Resultsmentioning

confidence: 92%

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

Jayachandran

Sundararaj

2015

Int. J. Fuzzy Syst.

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“…In another work, Jayachandran and Dhanasekaran et al 33 have proposed a robust brain tumor classification method, which focused on the structural analysis on both tumorous and normal tissues. The proposed system consists of preprocessing, segmentation, feature extraction, and classification.…”

Section: Related Workmentioning

confidence: 99%

“…In another work, Jayachandran and Dhanasekaran et al . have proposed a robust brain tumor classification method, which focused on the structural analysis on both tumorous and normal tissues.…”

Section: Related Workmentioning

confidence: 99%

Identification and classification of brain tumor through mixture model based on magnetic resonance imaging segmentation and artificial neural network

Deepa¹,

Emmanuel

2016

Concepts Magnetic Resonance

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Detection of brain tumor in Magnetic Resonance Imaging (MRI) is vital as it delivers data about unusual tissues which is essential for planning treatment. Automating this method is challenging due to the high variety in appearance of tumor tissue among dissimilar patients and in many circumstances, comparison between tumor and normal tissue. In this article, we presented a mixture model based segmentation and classification of brain MRI for tumor identification. The proposed robust mixture estimator combining trimming of the outliers is based on component wise confidence level ordering of observations. The proposed method consists of three stages. In the first stage, the brain MRI is segmented into white matter (WM), gray matter (GM), Cerebrospinal fluid (CSF), and outliers by ordering of observations. In second stage, outliers consists of tumor cells in which eight type of features Contrast, Correlation, Homogeneity, Energy, Entropy, Standard deviation, Skewness, and Kurtosis are extracted. In the third stage, the extracted features are trained by Artificial Neural Network (ANN)and based on this a brain tumor identification scheme is established to examine those features to judge whether brain tumor is present in the given image or not. Experimental results indicate that the proposed classification method has achieved 93.47% in sensitivity, 100% in specificity, and 96.34% accuracy with less computational time based on the number of extracted features when compared to earlier classification methods.

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“…It analyzes the spatial correlation between neighboring pixel color and edge orientation based on special texton types. 14 …”

Section: Micro Structure Descriptormentioning

confidence: 99%

Modified Micro Structure Descriptors and Hybrid-RBF Kernel SVM Based Diagnosis of Brain Tumor in MRI Images

Prabin¹,

Veerappan²

2015

J Med Imaging Hlth Inform

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Magnetic Resonance Images has become a widely used method of high quality medical imaging system, especially in brain imaging, where the soft-tissue contrast and non invasiveness is a clear advantage. Medical image classification is a pattern recognition technique in which different images are categorized into several groups based on some similarity measure. One of the significant applications is the tumor type identification in abnormal MRI brain images. The proposed system comprises feature extraction and classification. In feature extraction, some specific features are extracted using texture as well from intensity using modified Micro Structure Descriptors. The hybrid RBF kernel is designed in the classification stage and applied to training support vector machine to perform automatic detection of tumor in MRI images. The accuracy level (94%) for our proposed approach is proved at detecting the tumors in the brain MRI images. The obtained results depict that the proposed brain tumor detection approach produces better results in terms of k-fold cross validation method.

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Severity Analysis of Brain Tumor in MRI Images Using Modified Multi-texton Structure Descriptor and Kernel-SVM

Cited by 35 publications

References 18 publications

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

Identification and classification of brain tumor through mixture model based on magnetic resonance imaging segmentation and artificial neural network

Modified Micro Structure Descriptors and Hybrid-RBF Kernel SVM Based Diagnosis of Brain Tumor in MRI Images

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