Preprocessing and pectoral muscle separation from breast mammograms
نویسندگان
چکیده
Computer aided diagnosis (CAD) systems can be used as a second opinion to the radiologists for diagnosis of breast cancer from mammogram images. In this paper, we have proposed preprocessing method to remove noise from mammogram images. Then, enhancement has been performed. After that, background has been removed. Finally, pectoral muscle separation has been performed. It has been noted that results are very much satisfactory. This can be used further to improve the accuracy of diagnosing breast mammogram. We have used MIAS data set for experimentation purpose.
منابع مشابه
Automatic Identification and Elimination of Pectoral Muscle in Digital Mammograms
Computer aided detection/diagnosis aims at assisting radiologist in the analysis of digital mammograms. Digital mammogram has emerged as the most popular screening technique for early detection of breast cancer and other abnormalities in human breast tissue. The pectoral muscle represents a predominant density region in most mammograms and can affect/bias the results of image processing methods...
متن کاملNational Institute of Technology Calicut Department of Computer
Breast cancer is the most common form of cancer among women in many regions of India. One out of every 22 women is diagnosed with breast cancer. Mammography acts as an early detection tool for breast cancer and can detect tumors up to two years before it can be felt. Numerous studies have shown that the early detection saves lives and increases treatment options. Mammography is one of the X-ray...
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In most of the approaches of computer-aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pecto...
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Due to drastic growth in mammography, a huge number of high quality and diverse images are available for analysis. At this juncture, usage of computer vision techniques, which includes artificial systems to analyze these medical images, is indispensable. However, the usage of artificial systems for mammogram analysis is not new to this field. Though Computer-Aided Detection (CAD) for breast can...
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Pectoral muscle identification is often required for breast cancer risk analysis, such as estimating breast density. Traditional methods are overwhelmingly based on manual visual assessment or straight line fitting for the pectoral muscle boundary, which are inefficient and inaccurate since pectoral muscle in mammograms can have curved boundaries. This paper proposes a novel and automatic pecto...
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