The use of moving contrast sweep to increase the effectiveness of linear, non-linear and midtone shift contrast enhancement in mammography.
ABSTRACT Low contrast in mammographic image has always made detection of subtle signs such as the presence of micro calcification within dense tissue a challenge. Therefore, numerous researches have been conducted on contrast enhancement technique for mammographic images. However, most of these methods focus on enhancing specific range of intensity level producing an enhanced version of the original image. This paper proposes a new approach in dealing with contrast enhancement for mammographic images. Instead of producing just a single image, the approach proposed here will generate multiple different images where each of them corresponds to a different range of intensity level. These images will then be combined together to form frames of a moving video which would display seamless transition from one intensity level to another. As a result, the radiologist who is performing visual inspection of the image can sweep through the entire range of intensity level of the image which has been contrast enhanced.
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ABSTRACT: Breast cancer continues to be a significant public health problem in the United States. Primary prevention seems impossible since the causes of this disease still remain unknown. Early detection is the key to 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 number of mammograms generated in widespread screening. Automated breast cancer diagnosis attracts much attention of the researchers. However, the fuzzy nature of the mammograms and the low contrast between the breast cancer and its surroundings make the automated breast cancer detection very difficult. Mammogram contrast enhancement is critical and essential to breast cancer diagnosis.This paper uses an adaptive fuzzy logic contrast enhancement method to enhance mammographic features. We first normalize the mammograms to reduce the effects of different illuminations. Then, we fuzzify the normalized images based on the maximum fuzzy entropy principle. The local contrast is measured and enhanced by utilizing both the global and local information so that the fine details of mammograms can be enhanced and the noise can be suppressed. The histogram of the mammogram provides the global information and the fuzzy entropy of local window is computed to analyze the local information. Then, we use both the global and local information to define and enhance the contrast. Finally, the defuzzification is performed to transform the enhanced mammogram back to the spatial domain. The experiments demonstrate that the proposed method can effectively enhance the contours and fine details of the mammographic features which will be useful for breast cancer diagnosis.Information Sciences 12/2002; · 3.64 Impact Factor
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ABSTRACT: Mammograms are difficult to interpret, especially of cancers at their early stages. In this paper, we analyze the effectiveness of our adaptive neighborhood contrast enhancement (ANCE) technique in increasing the sensitivity of breast cancer diagnosis. Seventy-eight screen-film mammograms of 21 difficult cases (14 benign and seven malignant), 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a high-resolution of about 4096 x 2048 x 10-bit pixels and then processed with the ANCE method. Unprocessed and processed digitized mammograms as well as the original films were presented to six experienced radiologists for a receiver operating characteristic (ROC) evaluation for the difficult case set and to three reference radiologists for the interval cancer set. The results show that the radiologists' performance with the ANCE-processed images is the best among the three sets of images (original, digitized, and enhanced) in terms of area under the ROC curve and that diagnostic sensitivity is improved by the ANCE algorithm. All of the 19 interval cancer cases not detected with the original films of earlier mammographic examinations were diagnosed as malignant with the corresponding ANCE-processed versions, while only one of the six benign cases initially labeled correctly with the original mammograms was interpreted as malignant after enhancement. McNemar's tests of symmetry indicated that the diagnostic confidence for the interval cancer cases was improved by the ANCE procedure with a high level of statistical significance (p-values of 0.0001-0.005) and with no significant effect on the diagnosis of the benign control cases (p-values of 0.08-0.1). This study demonstrates the potential for improvement of diagnostic performance in early detection of breast cancer with digital image enhancement.IEEE Transactions on Information Technology in Biomedicine 10/1997; 1(3):161-70. · 1.98 Impact Factor