IJOART Volume 4, Issue 3, March 2015 Edition


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Kalapatapu Ravikiran Sharma, Prof. Shankar Das[]


In the healthcare system, sub-centre is the most peripheral level of contact with the community covering an average of 3000 population in rural areas, but in Andhra Pradesh it is effectively serving 4424 population on an average. In order to do justice to the larger population, the state government has divided the geographical area of each sub-centre into two parts and has allocated one part to 1st Auxiliary Nurse and Midwife (ANM) and the other part to the 2nd ANM. The sub-centre is a focal public health institution in the National Rural Health Mission (NRHM) which was launched in April 2005. The NRHM aims at strengthening state health systems with a special focus on reproductive and child health (RCH) services and disease control programmes.


S.Julian Savari Antony, Dr.S.Ravi[]


Breast cancer is one of the most emergent disease in women. Image classification is a supporting for medical system as well as the difficult task also. Mammographic images contain various features like space, distance, circumscribed masses and microcalcification. This paper addresses on classifying mammographic images created on the Region of Interest (ROI). The images obtained may be blurred due to the variation in illumination, intensity and contrast which cannot be directly processed. It will be discussed two stages for classify the image. At first stage, the quality of image is increased using histogram equalization method which normalizes the image. At second stage, the intensity features are computed from the image of shape features, region features such as 'Solidity', 'Eccentricity', 'Convex Area', 'Orientation', 'Perimeter', 'Major Axis Length', 'Minor Axis Length' which are extracted to compute volumetric values. The proposed classification method is reduced by the false positive result arise in mammogram classification and overcome missing features while using single feature mammogram classification. Therefore, the quality of the image classification is a final aim of this paper that is improved. The result shows that the proposed method has increased the classification accuracy.



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