• Journal of The Korean Radiological Technologist Association
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• v.28 no.1
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• pp.67-73
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• 2002

Purpose : For the woman, compression of breast during mammogram introduce pain that woman used to have fear for mammogram. Therefore, the purpose of this study was to determine the adequate and minimized compression power for mammogram with maintaining th

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.322-328
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• 2015

This study evaluated the diagnostic value and compares the Mammogram Tomosynthesis, and as compared to the AGD, was studied with respect to utilization of Tomosynthesis. During January 2015 one month were enrolled patients admitted to 62 people present. The ACR phantom was used. AEC was set to be. kVp is fixed and given a step-by-step changing the mAs analyzed AGD. Tomosynthesis was superior to the distinction of breast lesions when compared with Mammogram showed a noticeable difference in contrast. AGD(Average Glandular Dose) was higher 0.33 mGy. However, in the long run, the dose was reduced. Tomosynthesis is therefore increase the diagnostic value of the breast, a examination that can reduce the dose.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.128-132
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• 1998

유방암의 조기진단을 위한 수단으로 Mammography의 x-선 film-screen이 많이 사용된다. 그러나, Mammogram에서 정상조직과 암조직 간의 대조도 차이가 크지 않으므로 판독은 그다지 쉽지가 않다. 이러한 문제들의 해결을 위하여 mammogram의 디지털 화상처리 및 분석 연구가 활발히 진행 중이다. 본 연구에서는 진단방사선의들이 필름을 판독할 때 시각적인 인지도를 높여주고, 보다나은 의료지원 서비스의 제공을 위한 목적으로, 유방암의 조기진단의 중요한 요소인 미세석회의 검출을 위한 방법으로서 fractal dimension을 구하여 종괴와 미세석회, 미세석회에 대한 차이를 분석하고자 하였다. 각각의 실험군에 대하여 30명씩 60명의 데이터를 0.1mm resolution의 12bit gray scale로 획득하여 사용하였는데, 일차로 화상의 대조도 개선을 위하여 처리를 하였고 화상의 분석으로 강조된 화상의 불규칙정도 및 거친 정도를 나타내기 위하여 fractal dimension을 계산하였다. 원화상에서 가시적으로 분간하기 힘들었던 병변을 화상처리를 통해 강조된 화상에서는 쉽게 그 특징을 볼 수 있었다. 실제로 mammogram을 진단할 때, 강조화상으로 미세석회와 같은 조기진단의 가시적인 판단을 도모할 수 있으며, 미세석회의 진단에서 fractal dimension값을 이용하여 병변 특성의 하나로서 사용할 수 있을 것으로 판단된다.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.6-12
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• 2013

Mammography is the most common technique for the early detection of breast cancer. To diagnose correctly and treat of breast cancer efficiently, many image enhancement methods have been developed. This paper presents a nonlinear image enhancement method for the enhancement of digital mammogram. The proposed method is composed of a nonlinear function for brightness improvement and a nonlinear filter for contrast enhancement. The nonlinear function improves the brightness of dark area and extends the dynamic range of bright area, and the nonlinear filter efficiently enhances the specific regions and objects of the mammogram. The final enhanced image was obtained by combining the processed image with the nonlinear function and the filtered image with the nonlinear filter. The proposed nonlinear image enhancement method was confirmed the enhanced performance comparing with other existing methods.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.165-172
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• 2009

According to the American Cancer Society, breast cancer is the second largest cause of cancer deaths and most frequently diagnosed cancer in women. The currently most popular method for early detection of breast cancer is the digital mammography. A mass or calcification lesion has been known as the most important clue for the diagnosis. In this paper, we propose a diagnosis approach based on fuzzy cluster knowledge base. We combine different two sources of feature data in duel OFUN-NET and produce the diagnosis result with possibility degree. We also present the experimental results on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. These results show higher classification accuracy than conventional methods and the feasibility as a decision supporting tool for diagnosis of digital mammogram.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.162-168
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• 2007

For the past decade, the full-field digital mammography has been widely used for early diagnosis of breast cancer, and computer aided diagnosis has been developed to assist physicians as a second opinion. In this study, we try to predict the breast cancer using both mediolateral oblique(MLO) view and craniocaudal(CC) view together. A skilled radiologist selected 35 pairs of ROIs from both MLO view and CC view of digital mammogram. We extracted textural features using Spatial Grey Level Dependence matrix from each mammogram and evaluated the generalization performance of the classifier using Support Vector Machine. We compared the multi-view based classifier to single-view based classifier that is built from each mammogram view. The results represent that the multi-view based computer aided diagnosis in digital mammogram could improve the diagnostic performance and have good possibility for clinical use to assist physicians as a second opinion.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3595-3600
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• 2012

Objective: The objective of this study was to determine the practice and barriers of mammography and associated factors among Malaysian women in the general population. Methodology: A cross-sectional study was conducted among 200 women in Shah Alam, Selangor; Malaysia. The questionnaire contained 27 questions and was comprised of two sections; socio-demographic characteristics and practices, knowledge and barriers of mammography. All the data were analyzed using the Statistical Package for the Social Sciences (SPSS) 13.0. Results: Of the 200 Malaysian women who participated in this study, the majority were under the age of 50 years (65.5%), Malay (86%), and married (94.5%). Regarding any family history of cancer in general, the majority of the participants had none (78%). However, some did report a close relative with breast cancer (16.5%). While the majority of them knew about mammography (68%), 15% had had a mammogram once in their life and only 2% had the procedure every two or three years. Univariate analysis showed that age, family history of cancer, family history of breast cancer, regular supplement intake, regular medical check-up and knowledge about mammogram were significantly associated with mammogram practice among the general population (p=0.007, p=0.043, P=0.015, p=0.01, p=0.001, p<0.001; respectively). Multivariate analysis using multiple linear regression test showed that age, regular medical check-up and knowledge about mammography testing were statistically associated with the practice of mammography among the general population in Malaysia (p=0.035, p=0.015 and p<0.001; respectively). Lack of time, lack of knowledge, not knowing where to go for the test and a fear of the test result were the most important barriers (42.5%, 32%, 21%, 20%; respectively). Conclusion: The practice of mammogram screening is low among Malaysian women.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.148-150
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• 2006

In this paper, we propose a new method for detection of mass-type breast cancer in dense mammogram. As the proposed method analyzes texture of the breast tissue using method by fusing Homogeneity and Ranklets, improve problem of traditional method. Homogeneity gives the measure of uniform density, and Ranklets determine orientation selective property at vertical, horizontal and diagonal in mass region. The proposed method is suitable to dense mammogram with tangled normal tissue and cancer tissue. SVM(Support Vector Machine) classifier is used for effective detection of mass-type breast cancer in dense mammogram.