Classification of brain MR images using wavelets texture features and k-Means classfier

Gonal, Jayalaxmi S.; Kohir, Vinayadatt V.

doi:10.1109/eesco.2015.7253749

Cited by 1 publication

(1 citation statement)

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“…The authors proved that the SPFCM had a better performance than some of the FCM based algorithms. A classification method to classify brain magnetic resonance images as normal and abnormal using wavelets texture features and k-means classifier was proposed in [18]. Here, the Euclidean distances were measured between feature vector of test magnetic resonance image and k-means classifier was fed with reference magnetic resonance images.…”

Section: Review Of Literaturementioning

confidence: 99%

Hybrid enhanced ICA & KSVM based brain tumor image segmentation

Bathini

Gadgay

2018

IJEECS

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<span>Medical image processing is an important aspect in diagnosis and treatment strategy. The tremendous volume of medical data has accelerated the need for automated analysis of this image, more so in the case Magnetic Resonance Imaging (MRI). An improved K-means algorithm and EM algorithm have been combined in the proposed approach to produce a hybrid strategy for better clustering and segmentation using Enhanced ICA. A classifier for based on Support Vector Machine (SVM) has been formulated and employed for the classification of brain tumors in Magnetic Resonance Images (MRI). The proposed SVM classifier used a kernel in the form of Gaussian radial basis function kernel (GRB kernel) to improve the classifier performance. The performance of the classifier has been validated through expert clinical opinion and calculation of performance measures. The results amply illustrate the suitability of the proposed classifier.</span>

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Section: Review Of Literaturementioning

confidence: 99%