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M.N. Giri Prasad

Work place: Department of ECE, J.N.T.U.A, Anantapuramu, Andhra Pradesh, India

E-mail: mahendragiri1960@gmail.com

Website:

Research Interests: Image Processing, Image Manipulation, Image Compression

Biography

Dr. M. N. Giri Prasad received B.Tech Degree in Electronics & Communication Engineering from JNT University, Hyderabad, India. He received his M.Tech Degree from S.V.University, Tirupathi, India. He has received PhD in Biomedical Signal Processing from JNT University, Hyderabad, India. At Present he is Professor in Dept. of ECE at JNTUCE, Anantapur, A.P., India. He also worked as Principal at JNTUCE, Pulivendula, A.P., India. He has more than 20 years of teaching experience. He has more than 30 publications in standard International/technical Journals. His research interests include Biomedical Signal Processing, Antennas and Image Processing. He is a member of Professional societies like IE (India), NAFEN (India), ISTE (India), IACSIT (Singapore),CSTA(USA) and IAENG (Hongkong). He is reviewer of Engineers Australia Technical Journals, Australia, of International Journal of Computer Science and Information Security (IJCSIS), Pittsburgh, USA, International Journal of Electrical, Electronics and Computer Systems (IJEECS), USA and International Journal of Algorithm, of AWEM Group, USA. He is reviewer of National/International Conferences in India and IEEE Sponsored International Conferences held in Singapore and China. He chaired many National/International conferences in India. He is BOS member of reputed engineering colleges in Andhra Pradesh.

Author Articles
OTSU's Thresholding with Back Projection Modeling for Neural Network Data Sets

By S.Asif Hussain D. Satya Narayana M.N. Giri Prasad

DOI: https://doi.org/10.5815/ijigsp.2014.07.07, Pub. Date: 8 Jun. 2014

For Tracking interfaces and shapes which depends on the regions of pixel intensity is a challenging task in image segmentation. Many level set methods have been formulated for region based and edge based models in computer aided diagnosis systems. In order to provide accurate modeling involving numerical computations, contours, lesions and bias variance which often rely on pixel intensity variations for the region of Interest. The proposed method involves the formulation by deriving a global criterion function in terms of neighborhood pixels to represent domain field and bias variance characteristics. Gaussian impulse is used for smoothening sharp edges. Computational neural networks provide the integral part of most learning algorithms as images consists of redundant attributes of data which have redundant network connections with different input patterns of small weights form a network training process for minimizing the energy and to estimate the bias field correction for various imaging modalities. The PET and CT images are used as inputs which are affected with cancer; in order to extract the features, proposed method is used for easy diagnosis. The result shows the improved performance with Neural Networks and provides valuable diagnostic information.

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