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Intelligent Computer Aided Diagnosis (CAD) Systems can be used for detecting Microcalcification (MC) clusters in digital mammograms at the early stage. CAD systems help radiologists in identifying tumor patterns in an efficient and faster manner than other detection methods. In this paper, we propose a new approach for detecting tumors in mammograms using Radial Basis Function Networks (RBFNN). Prior...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
Mammography is the most effective method for the early diagnosis and treatment of breast Cancer diseases. However, data sets collected by image sensors are generally contaminated by noise. This ensures the need for image enhancement to aid interpretation. This paper introduces an efficient enhancement algorithm of digital mammograms based on wavelet analysis and modified mathematical morphology. In...
Medical images like mammograms are very difficult to analyze because of their low contrast. Different fractal features are used for analyzing mammograms in this paper. The new fractal feature derived from the modified average image is found to be a better feature for distinguishing between normal, malignant, benign and mammograms with microcalcifications. The study is performed on the mammograms obtained...
Strong evidence shows that characteristic patterns of breast tissues as seen on mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative evaluation of the characteristic mixture of breast tissues can be used as for mammographic risk assessment as well as for quantification of change of the relative proportion of different breast...
The presence of microcalcifications clusters, which appear as small bright spots in mammographic images, can be considered as a very important sign for breast cancer diagnosis. They can, however, be hard to detect due to their size and low contrast from surrounding normal tissue. In this paper, a new fuzzy-based method is presented to provide an appropriate segmentation of microcalcifications. This...
2D wavelet transform decomposition is widely used in computer aided detection of microcalcifications in mammograms. The aim of this paper is to investigate the better type of wavelet and its optimal potential level of decomposition that gives us better detection. Our algorithm consists of four steps: first, dimension reduction is performed on the mammography images to delimitate the ROI (region of...
One of the leading diseases in women is breast cancer. The detection in an earlier stage is done by indicating the presence of microcalcification or mass. We develop two detection systems that can help a radiologist to detect microcalcifications and masses in mammograms. In particular, we utilize Mamdani inference system with four features, i.e., B-descriptor, D-descriptor, average intensity inside...
In this paper, a set of spectral domain features based on the discrete cosine transform DCT of mammograms are extracted from the X-ray image, the extracted features by the proposed methods are exploited to classify regions of interest ROIs into positive ROIs containing clustered microcalcifications and negative ROIs containing normal tissues. A three-layer back-propagation neural network is used as...
We present the computer aided diagnosis system being developed to help experts in screening mammography. It is a very important project because about 8% of women develop breast cancer in her lifetime therefore global screening is necessary. It means that reliable diagnosis of huge number of images must be solved. The basic architecture of the system and the information processing need is presented...
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