No abstract

Breast Cancer is the most common type of cancer among women. In addition to the burden of mortality and morbidity, Breast Cancer treatment cost and expenditure comprises a major chunk of the catastrophic health expenditure and financial burden incurred by the population. Delayed hospital presentation and diagnosis augments the hardships faced by individuals, family, community, and the health system at large. The need and urgency to create awareness about the disease and enable early detection via community level population-based screening was identified early in 2014. Through the awareness sessions, the program aims to demystify Breast Cancer and break the stigma associated with it. The intervention adopted a screening methodology that was cost effective and combination of Clinical Breast Examination and Mammography was followed. This paper discusses the learnings and outcomes of the surveillance over the years across geographies spreading awareness.

This paper proposes a new image denoising algorithm using wavelets. It utilizes the pertinence of the neighbor wavelet coefficients by using the block thresholding scheme. Proposed enjoys a number of advantages over the other conventional image denoising methods. The aim of this paper is to investigate a multiresolution technique and the corresponding thresholding methods for image denoising. Consideration may also be given to applying some enhancement techniques to the existing methods so as to achieve both noise reduction and feature preservation. The noise acceptance and rejections rates have been computed for the existing techniques and the newly developed technique. The proposed technique provides better results with the soft thresholding and block thresholding based on parameters, MSE and PSNR General TermsGray Scale Image, Multiresolution Technique