T.V. Madhusudhana Rao

Work place: Department of CSE, T.P.I.S.T., Bobbili, A.P., INDIA

E-mail: madhu11211@gmail.com


Research Interests: Data Structures and Algorithms, Data Mining, Image Processing, Image Manipulation, Image Compression, Computer Vision


Mr. T.V. Madhusudhana Rao is currently working as an Associate Professor in the Department of Computer science and engineering at T.P.I.S.T., Bobbili. He received his B.Tech from JNT University, Kakinada, India and M.Tech from JNT University Anantapur, India. He is pursuing his PhD in the Department of Computer science and engineering, at JNT University, Kakinada, India, in the area of Content based image retrievals. His other research interests include Image Processing, Knowledge Discovery and Data Mining, Computer Vision and Image Analysis. He has published more than 10 papers in National/ International Conferences and Journals.

Author Articles
An Efficient System for Medical Image Retrieval using Generalized Gamma Distribution

By T.V. Madhusudhana Rao S.Pallam Setty Y.Srinivas

DOI: https://doi.org/10.5815/ijigsp.2015.06.07, Pub. Date: 8 May 2015

Efficient diagnosis plays a crucial role for treatment. In many cases of criticalness, radiologists, doctors prefer to the usage of internet technologies in order to search for similar cases. Accordingly in this paper an effective mechanism of Content Based Image Retrieval (CBIR) is presented, which helps the radiologists/doctors in retrieving similar images from the medical dataset. The paper is presented by considering brain medical images from a medical dataset. Feature vectors are to be extracted efficiently so as to retrieve the images of interest. In this paper a two-way approach is adopted to retrieve the images of relevance from the dataset. In the first step the Probability Density Functions (PDF) are extracted and in the second step the relevant images are extracted using correlation coefficient. The accuracy of the model is tested on a database consisting of 1000 MR images related to brain. The effectiveness of the model is tested using Precision, Recall, Error rate and Retrieval efficiency. The performance of the proposed model is compared to Gaussian Mixture Model (GMM) using quality metrics such as Maximum distance, Mean Squared Error, Signal to Noise Ratio and Jaccard quotient.

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