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Articles by S. Nirmala Devi
Total Records ( 3 ) for S. Nirmala Devi
  G. Saranya and S. Nirmala Devi
  Data embedding has wide range of applications in the medical field. This method is helpful in securing information of the patients from the intruders with high storage capacity. The medical images of different modalities like CT, MRI and PET with the digitized clinical data can be sent to the physicians across the world for the diagnosis. Due to the bandwidth and storage constraints, medical images along with the clinical data must be compressed before transmission and storage. This study presents a new technique for data embedding and retrieving the digitized clinical data along with the DICOM images by using Complex Contourlet Transform (CCT). It also estimates the compression method by using entropy encoder. Hence, this study suggests that the data embedding and retrieving technique based on Complex Contourlet Transform (CCT) is more efficient and also it has high hiding capacity. The improved values of Compression Ratio (CR), Space Saving (SS), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) show that the new technique satisfies the properties of the data embedding technique.
  S. Nirmala Devi and A. Pethalakshmi
  Since, the advent of networking, the computers are vulnerable to the attack of the intruders. To tackle this, trustworthy computing was adopted in the networking environment. The grid computing environment works on the agreed virtual organizations which necessitates the secured reservation schemes. There were various reservation schemes available in grid as FCFS, priority based reservation, reservation based on negotiation, TARR, optimal resource reservation, etc. in this grid environment, there is the possibility for an intruder to reserve the resource. The intruder may send the request for the resource and reserve it to block the actual process to access the resource. This paper deals with the application of challenge response authentication for resource reservation in grid computing.
  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.
 
 
 
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