Enhanced Image Processing Techniques Based On Technical Challenges Of Mammogram Image Characteristics

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Date
2010-04
Authors
Mohd Nordin, Zailani
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Publisher
Universiti Sains Malaysia
Abstract
Breast cancer is the leading type of cancer in Malaysia. More than 30% of total cancer case reported among women in Malaysia is made up of breast cancer. At the moment, one of the best known techniques used for breast cancer detection is mammography. Unfortunately, the image produced through mammography is normally noisy and low in contrast making detection of early signs of breast cancer (i.e. microcalcification and mass) difficult. Therefore, a lot of studies have been conducted to develop image processing techniques which would help in the detection of these early signs. However, most of these techniques were developed without a thorough study upon the mammogram image technical characteristics. Hence, an image which has been enhanced from one aspect may end up worst from another aspect. In this study, new image processing techniques have been developed based on findings which have been gathered from characterization of mammogram images. The characterization process covered the analysis of grey level distribution, noise, edge and texture. Detailed understanding of these characteristics, provide a solid basis for the development of new image processing techniques in this study. The new techniques brought forth through this study include contrast enhancement (i.e. Moving Contrast Sweep), noise suppression (i.e. Mean Approximation Adaptive Wiener Filter) and segmentation algorithm (i.e. Mean Median Crossing Segmentation). In addition to that, a new edge detection algorithm (i.e. Delta Variance Edge Detection) has also been developed to assist in microcalcification detection. Furthermore, a new classification system which is based on texture characteristic has also been developed for mass detection. Based on the analysis, it has been found that the Mean Approximation Adaptive Wiener Filter and Delta Variance Edge Detection techniques perform better than their predecessor. The performance of Mean Median Crossing Segmentation and Moving Contrast Sweep techniques is equivalent to existing techniques but they can be implemented through a more practical approach. The mass detection technique through statistical texture analysis demonstrates a lot of potential but still has room for improvement. It is hoped that, the new image processing techniques developed through solid understanding of mammogram image characteristic in this study could provide a strong foundation for the development of medical imaging applications in the future.
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Keywords
Image processing techniques based on technical Challenges , of mammogram image characteristics
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