• Proceedings of the Korean Society of Medical Physics Conference
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• 1999.11a
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• pp.287-288
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• 1999

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4723-4726
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• 2014

Background: Breast cancer is the second most common cancer in the world and by far the most frequent cancer among women. Objective: The present study was undertaken to assess the awareness of breast cancer warning signs and screening methods among the women of Pokhara valley, Nepal. Materials and Methods: A cross-sectional questionnaire survey was carried out in a community setting with the female population. The questionnaire was administered in face-to-face interviews by trained research assistants. Results: Nepalese women demonstrated poor awareness of warning signs like a breast lump, lump under the armpit, bleeding or discharge from the nipple, pulling of the nipple, changes in the position of the nipple, nipple rash, redness of the breast skin, changes in the size of the breast or nipple, changes in the shape of the breast or nipple, pain in the breast or armpit, and dimpling of the breast skin. While 100% of nurses were aware about breast self-examination(BSE), mammography and warning signs of breast cancer. Levels of knowledge were significantly poorer in women with other occupations. Graduates were more aware about BSE, mammogram and warning signs of breast cancer compared to those with other educational levels. Conclusions: The findings indicated that the level of awareness of breast cancer, including knowledge of warning signs and BSE, is sub-optimal among Nepalese women.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.80-88
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• 2006

In this paper we developed a novel system for automatic detection of mass type breast cancer on dense digital mammogram images. The new approaches presented in this paper are as follows: 1) we presented a method that stably decides the mass center and radius without being affected by image signal irregularity. 2) We developed a radial directional filter that is suitable to process mass image signal. 3) And we developed the multiple feature function based on mass shape spiculation, mass center homogeneity, and mass eccentricity, so as to determine mass-type breast cancer. When the proposed system is applied to dense mammographic images, the true 기arm rate is improved by 10% over a conventional system while the false alarm is increased by 1 per image.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.17
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• pp.7309-7316
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• 2014

Background: While many researchers often use a theoretical framework for mammogram repeat interventions, it seems they do not apply an identified mediation analysis method. The aim of this study was to determine the mediators of mammogram replication behavior in two tailored interventions for non-adherent Iranian women. Materials and Methods: A sample population of 184 women over 50 years old in Sanandaj, Iran, was selected for an experiment. Participants were randomly allocated into one of the three conditions: 1) an intervention based on the Health Belief Model (HBM) 2) an intervention based on an integration of the HBM and selected constructs from the Theory of Planned Behavior (TPB), and 3) a control group. Constructs were measured before the intervention, and after a 6-month follow-up. Results: Perceived self-efficacy, behavioral control, and subjective norms were recognized as mediators in the HBM and selected constructs from the TPB intervention. Perceived susceptibility, severity, barriers, self-efficacy and behavioral control met the criteria for mediation in the HBM intervention. Conclusions: This study was successful in establishing mediation in a sample of women. Our findings enrich the literature on mammography repeat, indicating key intervention factors, and relegating redundant ones in the Iranian populations. The use of strategies to increase mammography repeat, such HBM and TPB constructs is suggested to be important for maintaining a screening behavior, once the behavior has been adopted.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.721-734
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• 2001

This paper proposed automated methods for the detection of breast mass. We analysed characteristic of the mass by using the features on mammograms. The homogeneity was used to distinguish mass and abnormal homogeneous tissue from the Cooper's ligament and multiple threshold method was used to deal with the high density candidates. By using the 8-connectivity, the first step candidates were selected. We generated the dualistic images of each candidate in which we regard the gray value as topographic height information. From these candidates, the second candidates were selected by comparing the circularity and the distribution rates. The final detection was done with the method in which we generated the template of each candidate and compared each other. From these methods, we grade the order from the candidate. We applied the algorithm to the 136 mammograms and compared to the radiologist's outlines of the leisions. The detection resulted that the sensitivity of the proposed methods was 93.38% and 97.63% FP(False positive) which we can segmented mass in the first grade in the 124 cases.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3443-3449
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• 2012

Introduction: Despite health education efforts to educate women on breast cancer and breast cancer screening modalities, the incidence of breast cancer and presentation at an advanced stage are still a problem in Malaysia. Objectives: To determine factors associated with the uptake of breast cancer screening among women in the general population. Methods: This pre-intervention survey was conducted in a suburban district. All households were approached and women aged 20 to 60 years old were interviewed with pre-tested guided questionnaires. Variables collected included socio-demographic characteristics, knowledge on breast cancer and screening practice of breast cancer. Univariate and multivariate analysis were performed. Results: 41.5% of a total of 381 respondents scored above average; the mean knowledge score on causes and risks factors of breast cancer was 3.41 out of 5 (SD1.609). 58.5% had ever practiced BSE with half of them performing it at regular monthly intervals. Uptake of CBE by nurses and by doctors was 40.7% and 37.3%, respectively. Mammogram uptake was 14.6%. Significant predictors of BSE were good knowledge of breast cancer (OR=2.654, 95% CI: 1.033-6.816), being married (OR=2.213, 95% CI: 1.201-4.076) and attending CBE (OR=1.729, 95% CI: 1.122-2.665). Significant predictors for CBE included being married (OR=2.161, 95% CI: 1.174-3.979), good knowledge of breast cancer (OR=2.286, 95% CI: 1.012-5.161), and social support for breast cancer screening (OR=2.312, 95% CI: 1.245-4.293). Women who had CBE were more likely to undergo mammographic screening of the breast (OR=5.744, 95% CI: 2.112-15.623), p<0.005. Conclusion: CBE attendance is a strong factor in promoting BSE and mammography, educating women on the importance of breast cancer screening and on how to conduct BSE. The currently opportunistic conduct of CBE should be extended to active calling of women for CBE.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.24-29
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• 2014

Digital mammography is the most common technique for the early detection of breast cancer. To diagnose the breast cancer in early stages and treat efficiently, many image enhancement methods have been developed. This paper presents a multi-scale contrast enhancement method in the Laplacian pyramid for the digital mammogram. The proposed method decomposes the image into the contrast measures by the Gaussian and Laplacian pyramid, and the pyramid coefficients of decomposed multi-resolution image are defined as the frequency limited local contrast measures by the ratio of high frequency components and low frequency components. The decomposed pyramid coefficients are modified by the contrast measure for enhancing the contrast, and the final enhanced image is obtained by the composition process of the pyramid using the modified coefficients. The proposed method is compared with other existing methods, and demonstrated to have quantitatively good performance in the contrast measure algorithm.

• Journal of the Korean Society of Radiology
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• v.8 no.1
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• pp.27-33
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• 2014

Medical imaging has increased rapidly in the increase of interest in health, with the development of computer technology, digitization of medical imaging is rapidly advancing, PACS has been introduced to the medical field. Increase in the production of medical images by these phenomena made increased the workload of radiologist who must read a medical image. in response to the need for secondary diagnosis using a computer, The term of CAD in medical radiology field was introduced. In this study, we have proposed a CAD algorithm for the interpretation of the image obtained by the digital X-ray mammography equipment. The experiments were performed by programmed in Visual C++ for the proposed algorithm. A result of the execution of the CAD algorithm seven sample images, the results of five samples was confirmed in breast cancer and benign tumors, both the images sample was error processing. If you use a program that implements this with the algorithm proposed in this study it is helpful to reading breast images, and it is considered to contribute significantly to the early detection of breast cancer.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.1-1
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• 2001

본 논문에서는 웨이블릿 변환을 멀티스케일 매치 필터의 관점에서 해석하고, 이를 위하여 마르코프 랜덤 필드에 묻혀있는 가우시안 형태의 작은 물체를 검출하는 이론적 근거를 제시하며, 이의 응용으로 맘모그램에 존재하는 마이크로캘시피케이션을 검출하는 알고리즘을 제안한다. 검출하고자 하는 물체가 가우시안 형태이고 그 스케일이 웨이블릿 변환에 의해 계산된 것과 일치하며, 그 주변의 잡영이 마르코프 프로세스이면, LoG(Laplacian of Gaussian) 웨이블릿은 멀티스케일 매치 필터로 작용하며, 적절한 디테일 이미지를 단순히 이진화함으로써 최적의 검출기를 구현할 수 있다. 그런데, 마이크로캘시피케이션은 정확한 가우시안 형태를 갖지 않고, 게다가 맘모그램의 배경이미지도 마르코프 프로세스라는 가정에서 벗어난다. 이러한 불일치를 해결하기 위하여, 본 논문에서는 멀티스케일 웨이블릿 계수에서 추출한 특징벡터를 Hotelling observer에 입력하여 처리함으로써 이를 보상하고자 하였다.