D. Devaraj

Work place: ECE Dept., Cape Institute of Technology, Levengipuram, India

E-mail: deva230@yahoo.com


Research Interests: Neural Networks, Network Architecture, Network Security, Database Management System, Algorithm Design, Combinatorial Optimization


Dr.D.Devaraj, completed his B.E (Electrical and Electronics Engineering) in 1992 and M.E (Power Systems) at Thiagarajar College of Engineering, Madurai, in 1992. He did his Ph.D (Power Systems) from IIT Madras, Chennai, in 2001. He has got more than 15 years of teaching experience and 15 years of research experience. He is a Ph.D Evaluator for Madurai Kamarajar University. He has guided 9 candidates for Ph.D scholars and presently guiding more than 10 candidates for Ph.D scholars. His areas of interest include Power System Security, Neural Network, Genetic algorithm, Power System Optimization. He wrote 2 books on Power system Analysis for III year Electrical Engineering Students, Basic Electrical Engineering for I year Mechanical & Bio Technology Students. He has published more than 40 papers in national journals and international journals and more than 100 papers in conference proceedings. 

Author Articles
Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

By G.Bharatha Sreeja P. Rathika D. Devaraj

DOI: https://doi.org/10.5815/ijmecs.2012.03.08, Pub. Date: 8 Mar. 2012

Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.

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