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Articles by M. Kavitha
Total Records ( 2 ) for M. Kavitha
  M. Kavitha and S. Karthik
  Mobile Ad-hoc Network (MANET) is a network which allows mobile servers and clients to communicate in a dynamic Infrastructure. MANET is a fast and increasing region of study as it discovers and utilizes diversified applications, in which the information should be well structured for accessibility and data should be put together in the database. In such databases, the mobile peer stores and access the data functionality such as storage, manage and reports in the database. In this study, the challenges of data replicas in the mobile database are focused. Data replication mainly focuses on availability and reliability of data in the mobile nodes. Data replication concentrates on certain scenarios like frequency disconnection, node mobility, server network partition and power. In proposed method, a cluster based data replication technique for replicating data and to overcome the problems related to data management problems in MANET environment. The proposed approach has three phases; selfish node detection, formation of cluster and cluster head selection and finally, data distribution to the respective cluster head. By NS2 simulation, the performance of the proposed approach is observed to be efficient with improved data consistency with minimal overhead and delay.
  Shirley Selvan , M. Kavitha , S. Shenbagadevi and S. Suresh
  Problem statement: Elastography is developed as a quantitative approach to imaging linear elastic properties of tissues to detect suspicious tumors. We propose an automatic feature extraction method in ultrasound elastography and echography for characterization of breast lesions. Approach: The proposed algorithm was tested on 40 pairs of biopsy proven ultrasound elastography and echography images of which 11 are cystic, 16 benign and 13 malignant lesions. Ultrasound elastography and echography images of breast tissue are acquired using Siemens (Acuston Antares) ultrasound scanner with a 7.3 MHz linear array transducer. The images were preprocessed and subjected to automatic threshold, resulting in binary images. The contours of a breast tumor from both echographic and elastographic images were segmented using level set method. Initially, six texture features of segmented lesions are computed from the two image types followed by computing three strain and two shape features using parameters from segmented lesions of both elastographic and echographic images. Results: These features were computed to assess their effectiveness at distinguishing benign, malignant and cystic lesions. It was found that the texture features extracted from benign and cystic lesions of an elastogram are more distinct than that of an ultrasound image .The strain and shape features of malignant lesions are distinct from that of benign lesions, but these features do not show much variation between benign and cystic lesions. Conclusion: As strain, shape and texture features are distinct for benign, malignant and cystic lesions, classification of breast lesions using these features is under implementation.
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