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Articles by N. Kumaravel
Total Records ( 4 ) for N. Kumaravel
  S. Nirmala Devi , M. Dhanalakshmi and N. Kumaravel
  We propose a method for segmentation of vascular structures and determination of blood flow velocity in coronary angiograms. The angiogram images of normal and abnormal-collateral patients are acquired at a rate of 15 frames/sec. In each frame, blood vessel is segmented from background using a backpropagation network. The input is given to two network topologies (121-17-2 and 4-3-2 layer configuration) and tested for their performance. The 4-3-2 configuration was able to classify blood vessel with less number of iterations comparatively and it can detect even small vessels with less computation time. The blood flow velocity in angiogram is determined in two methods. First method is by measuring the distance traversed by the contrast agent in each frame. The second method is based on determining the change in concentration of the contrast agent in two fixed region of interest. By first method, the flow velocity for normal and collateral angiograms are found to be 38 pixels/frame(p/f) and 15 p/f, respectively and by the second method, it is calculated as 45 and 28 p/f, respectively. The results show delayed arrival of contrast in abnormal collaterals than in normal images.
  M. Sasikala and N. Kumaravel
  The objective of image fusion is to combine the source images of the same scene to form one composite image that contains a more accurate description of the scene than any one of the individual source images. A comparison of various feature based fusion schemes is presented in this study. Feature extraction plays a major a role in the implementation of feature-level fusion approaches. Prior to the merging of images, salient features, present in all source images, are extracted using an appropriate feature extraction procedure. Then, fusion is performed using these extracted features. The performance of image fusion is evaluated by normalized least square error, entropy, overall cross entropy, standard deviation and mutual information. The experimental results show that the images fused with salience match measure, gradient and gradient match measure gives better performance.
  T. Kalpalatha Reddy and N. Kumaravel
  Bone architecture is an important factor that determines bone strength in addition to bone mass. Texture analysis of the trabecular bone pattern on axial dental CT is being investigated as a potential means to characterize the bone quality. In this study, we examined the use of an artificial neural network and features from different scales of curvelet transform analysis to obtain a measure related to bone architecture and quality. Texture features are extracted from 220 image regions of jaw bone CT images (both male and female) using spatial gray level dependence method, run length, histogram and curvelet transform. By using the Neural Network Classifier, the classification of bone samples at different locations of the jawbone region is performed. First the combination of the features from run length and first order statistics achieved overall classification accuracy ≥69.23%. Features selected from the curvelet based cooccurrence matrix performed better with overall classification accuracy >80%. In order to increase the success rate the classification is done using the combination of curvelet statistical features, run length and curvelet cooccurence features as feature vector and using this, a mean success rate of 97.2% is obtained.
  S. Nirmala Devi and N. Kumaravel
  Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. However, most present snake models cannot provide better capture range and evolution stop mechanism. This project presents a new external force for active contours, largely solving both problems. An extension of the gradient vector flow snake (GVF snake) method is presented. First, the adaptive balloon force has been developed to increase the GVF snake’s capture range and convergence speed. Then, a dynamic GVF force is introduced to provide an efficient evolution-stop mechanism. In this way, we prevent the snake from breaking through the correct surface and locking to other salient feature points. The active contour models have been applied on X-ray coronary angiogram images. The segmentation results demonstrate the potential of improved GVF method is comparison with all previous active contour methods. Texture parameters have been calculated and results are compared with all active contour models.
 
 
 
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