R. Sukanesh Rajamony

Thiagarajar College of Engineering, Mathurai, Tamil Nādu, India

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Publications (3)1.01 Total impact

  • A. Padma Nanthagopal, R. Sukanesh Rajamony
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    ABSTRACT: Computational methods are useful for medical diagnosis because they provide additional information that cannot be obtained by simple visual interpretation. As a result, an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. In this paper, we present the combination of wavelet statistical texture features (WST) obtained from two-level discrete wavelet-transformed (DWT) images and wavelet co-occurrence texture features (WCT) obtained from two-level DWT detail images for the classification of abnormal brain tissues into benign, malignant tumor of CT images. Our proposed system consists of four phases: (1) segmentation of region of interest, (2) discrete wavelet decomposition, (3) feature extraction and feature selection, and (4) classification and evaluation. The support vector machine is employed to segment the shape of tumor information. A combination of both WST and WCT texture features is extracted from tumor region of two-level discrete wavelet-transformed images. Genetic algorithm (GA) is used to select the optimal texture features from the set of extracted features. The probabilistic neural network classifier (PNN) is built to classify the abnormal brain tissues into benign, malignant tumor images and evaluate the performance of classifier by comparing the classification results of the PNN classifier with linear vector quantization (LVQ) neural network classifier, back propagation neural network (BPN) classifier. The results of PNN, LVQ, BPN classifiers for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic analysis. From the experimental results, it is inferred that the best classification performance is achieved by PNN than LVQ and BPN classifiers. The system has been tested with real data of 80 benign, malignant CT brain tumor images and has achieved satisfactory results. Graphical Abstract
    Journal of Visualization 02/2013; 16(1). · 0.51 Impact Factor
  • A. Padma Nanthagopal, R. Sukanesh Rajamony
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    ABSTRACT: Automated and accurate classification of computed tomography (CT) images is an integral component of the analysis and interpretation of neuro imaging. In this paper, we present the wavelet-based statistical texture analysis method for the classification of brain tissues into normal, benign, malignant tumor of CT images. Comparative studies of texture analysis method are performed for the proposed texture analysis method and spatial gray level dependence matrix method (SGLDM). Our proposed system consists of five phases (i) image acquisition, (ii) discrete wavelet decomposition (DWT), (iii) feature extraction, (iv) feature selection, and (v) analysis of extracted texture features by classifier. A wavelet-based statistical texture feature set is derived from two level discrete wavelet transformed approximation (low frequency part of the image) sub image. Genetic algorithm (GA) and principal component analysis (PCA) are used to select the optimal texture features from the set of extracted features. The support vector machine (SVM) is employed as a classifier. The results of SVM for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic (ROC) analysis. The experimental results show that the proposed system is able to achieve higher classification accuracy effectiveness as measured by sensitivity and specificity. Graphical Abstract
    Journal of Visualization 11/2012; 15(4). · 0.51 Impact Factor
  • A Padma Nanthagopal, R Sukanesh Rajamony
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    ABSTRACT: The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.
    Journal of Medical Engineering & Technology 05/2012; 36(5):271-7.

Publication Stats

2 Citations
1.01 Total Impact Points

Institutions

  • 2013
    • Thiagarajar College of Engineering
      • Department of Electronics and Communication Engineering
      Mathurai, Tamil Nādu, India