International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 7, Issue 2 (May 2013), PP. 06-11 6 Segmentation Approach for Detecting Micro-Calcified Region in Bosom Ms.Jayashree R.Parate 1 ,Prof.R.K.Krishna 2 1 Reasearch Scholar, Rajiv Gandhi College Of Engg And Research Chandrapur. 2 Asst Prof., Rajiv Gandhi College Of Engg And Research Chandrapur Abstract:- An image processing application was developed in C++ for the improvement of mammographic images. Wavelet-based image enhancement was implemented by processing the DWT detail coefficients with a sigmoid function. Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as micro calcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. .images are of low contrast so here require denoising and the process is called preprocessing. 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. These wavelet were applied to 130 digitized mammograms. The mammograms gone under processing were blind-reviewed by an expert radiologist. A number of mammographic image parameters, such as definition of masses, vessels, microcalcifications, etc. were checked. Filter performances were assessed by thresholding analysis of the physician’s evaluation. Processing time was less than 3s for the wavelet-based and hyperbolic filters in a typical desktop. Keywords :- wavelet based Thresholding, breast cancer,mammography, window based Thresholding, segmentation. I. INTRODUCTION Breast cancer is now a days is one of the major cause of death in women worldwide. Breast cancer currently affected for more than 38% of cancer incidence and a significant percentage of cancer mortality in both developing and developed countries. It has been shown that early detection and treatment of breast cancer are the most effective methods of reducing mortality .manual checking for this disease is labour intensive . mammography is the method of choice for early breast cancer detection [6]-[8]. Although automated analysis of mammograms cannot fully exchange the concept of radiologists, an accurate computer-aided analysis method can help radiologists to make more accurate and efficient decisions .Tumors and other disabilities present in the mammograms are of area interests that need to be segmented and extracted in mammograms . Some of the grey scale based segmentation methods are good to extract the exact edges of same characteristics grayscale regions. They are ever not so good to extract the desired affected areas in mammograms with complex structure because of the complex distribution of the grayscale. However, the appearances of breast cancers are very substle and unstable in their early stages. Therefore, doctors and radiologists can miss the abnormality easily if they only diagnose by experience. The computer aided detection technology can help doctors and radiologists in getting a more reliable and effective diagnosis. There are numerous tumour detection techniques have been used by many authors to solve the queries related to cancer. Wavelet transform-based methods offer a normal original framework for providing multiscale image representations that can be separately explored .by using multiscale decomposition, almost of the gross intensity distribution can be merged in a large scale image, while the information about details and single characteristics, such as edges and textures, can be used in mid- to small scales. Here 1-D wavelet-based analysis is performed to find the Power density function and adaptively selected proper thresholds for segmentation by searching for the local minima of the 1-D wavelet transformed PDF. This method is simple, fast, and effective for segmenting tumors in mammilla. However, the method is not very good when the target and the background regions having little difference in gray-level values. According to the neighboring windows around the pixel a threshold is computed for each pixel of the image. It did not consider the case where a mass contains the small window, the center region of a suspicious lesion is not detected, and it gives an empty area in the segmentation result. In other words, the algorithm can obtain good detection results on one type of lesions, but it may generate unreasonable detection results on other types of lesions. An approach is used to segment the suspicious mass regions by a local adaptive thresholding technique after the mammograms are enhanced with a linear transformation filter. For each pixel of the image, a threshold is calculated according to the next placed windows around the pixel. Next, a decision is made to classify the pixel whether it belongs to a suspicious lesion or a normal region by the threshold. From the experimental results, we can see that this algorithm works best in mammographic mass detection. At the same scene, experiments show