Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. Pattern recognition in image processing requires the extraction of features from regions of the image and t...

BACKGROUND The cumulative risk of a false positive result from a breast-cancer screening test is unknown. METHODS We performed a 10-year retrospective cohort study of breast-cancer screening and diagnostic evaluations among 2400 women who were 40 to 69 years old at study entry. Mammograms or clinical breast examinations that were interpreted as indeterminate, aroused a suspicion of cancer, or...

In the past several years there has been tremendous interest in image processing and analysis techniques in mammography. One common approach for detecting abnormalities in mammograms [1, 2] is to use a series of heuristics, e.g. filtering and thresholding, which may include texture analysis to automatically detect abnormalities [3]. These heuristic methods suffer from a lack of robustness when ...

An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram s...

Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method is developed by combining feature-based and intensity-based image registration ...

In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learnin...

In the Kaiser Permanente (KP) Georgia Region, a program of extensive mailings, call center contacts, and other avenues of patient education has been designed to increase the number of women having mammograms and to decrease the mortality rate from breast cancer. Citing statistics from various studies nationwide and in the Atlanta area, the authors outline some reasons for patient resistance to ...

Classification and detection abnormal Mammography application is extremely valuable if there should arise an occurrence of analyze, checking and evaluating Lesion. Be that as it may, mechanized Lesion Detection in MAMMOGRAPHY postures many difficulties as to qualities of a picture. Numerous strategies are included to recognize the injury in the medicinal pictures such watershed, picture Detecti...

Image preprocessing algorithms for improving the performance of breast cancer detection algorithm have been evaluated and their performance are reported in this paper. Those algorithms are implemented successfully in different sequence combination, those are (i) intensity adjustment before removal of curvilinear structure and (ii) intensity adjustment after removal of curvilinear structure. Int...