S.Pallam Setty

Work place: Department of CS & SE, Andhra University, Visakhapatnam, INDIA

E-mail: drspsetty@gmail.com


Research Interests: Image Processing, Computer Networks, Solid Modeling, Image Manipulation, Image Compression, Computer Architecture and Organization, Computer Vision


Dr. S. Pallam Setty is currently working as a Professor in the Department of Computer Science and Systems Engineering at Andhra University, Visakhapatnam, India. He received his PhD in Computer Science and Systems Engineering from Andhra University, Visakhapatnam, India. He has 23 years of teaching and research experience. He has guided 4 students for Ph D and guiding 12 scholars for Ph D. His current research interests are in the areas of Image Processing, computer vision and image analysis, Computer Networks, Modeling and Simulation. He has more than 80 research papers in various reputed international journals and conference.

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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