Rajalakshmi N.,Sanson Engineers Ltd. |
Prabha V.L.,Government of Tamilnadu
Australian Journal of Electrical and Electronics Engineering | Year: 2013
This paper proposes an efficient classification technique that utilises colour- converted hybrid segmentation algorithm with multi-class-support vector machine classifier for an automated diagnosis and classification of brain magnetic resonance (MR) images. The texture, colour and shape features have been extracted and these features are used to classify MR brain images into three categories, namely, normal, benign and malignant. This paper utilises colour- converted segmentation algorithm which uses hybrid particle swarm optimisation plus K-means clustering technique. Feature selection is performed by a sequential floating forward selection. The method proposed makes use of classification technique based on multi-class support vector machine to classify MR brain images. The accuracy of the proposed system has been verified and found that accuracy of above 97% can be achieved. The proposed system can provide best classification performance with high accuracy, low error rate and lower computational effort. © Institution of Engineers Australia, 2013. Source
Rajalakshmi N.,Sanson Engineers Ltd.
European Journal of Scientific Research | Year: 2012
This paper proposes a color-based segmentation method that uses hybrid PSO (particle swarm optimization) +K-Means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The proposed approach is compared with the existing color-converted K-means clustering segmentation technique. The major drawback of existing technique is, it can only generate a local optimal solution due to its sensitiveness to initial partition. Particle Swarm Optimization (PSO) technique offers a globalized search methodology but suffers from slows convergence near optimal solution. The proposed approach combines the ability of globalized searching of the PSO algorithm and the fast convergence of the K-means algorithm to improve clustering and avoids being trapped in a local optimal solution. Comparative analysis in terms of segmentation efficiency, convergence rate is performed between the color based segmentation with K-means and the proposed technique. The results illustrate that the proposed method outperforms the existing method. © EuroJournals Publishing, Inc. 2012. Source
Rajalakshmi N.,Sanson Engineers Ltd. |
Lakshmi Prabha V.,Sanson Engineers Ltd.
International Journal of Imaging Systems and Technology | Year: 2015
The present article proposes a novel computer-aided diagnosis (CAD) technique for the classification of the magnetic resonance brain images. The current method adopt color converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on IGSFFS (Information gain and Sequential Forward Floating Search) and Multi-Class Support Vector Machine (MC-SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K-means algorithm called WFF-K-means and modified cuckoo search (MCS) and K-means algorithm called MCS-K-means, which can find better cluster partition in brain tumor datasets and also overcome local optima problems in K-means clustering algorithm. The experimental results show that the performance of the proposed algorithm is better than other algorithms such as PSO-K-means, color converted K-means, FCM and other traditional approaches. The multiple feature set comprises color, texture and shape features derived from the segmented image. These features are then fed into a MC-SVM classifier with hybrid feature selection algorithm, trained with data labeled by experts, enabling the detection of brain images at high accuracy levels. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. The proposed method provides highest classification accuracy of greater than 98% with high sensitivity and specificity rates of greater than 95% for the proposed diagnostic model and this shows the promise of the approach. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 226-244, 2015 © 2015 Wiley Periodicals, Inc. Source