• 대한디지털의료영상학회논문지
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• 제16권2호
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• pp.25-29
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• 2014

The full-field digital mammography (FFDM), which has been known as a digital breast imaging system, carries out more outstanding performance than the screen-film mammography in overall image quality, skin & nipple, description of pectoral muscle and expression of micro-calcification. Thus, in this thesis, I perform experiments for both the enhancement of image quality and accurate estimation of the result in question, when detecting the very tiny-sized lesions in mammography. The image of digital breast X-rays is the important diagnostic tool for detecting early breast cancer and micro calcification lesion. The experiment of how much compression rate has an effect on the result of diagnosis in the case of microcalcification lesion, with JPEG2000 40:1 compression and over 50% enlargement led to obscure or definitely unacceptable diagnostic results is performed. And in another study of assessment of PSNR degree. I recognized the importance of standardized management system in mammography, where not to mention the accurate reading of the image has the most crucial role in diagnosis

• 대한영상의학회지
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• 제84권2호
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• pp.361-371
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• 2023

영상 유도하 유방 조직검사의 성공 여부는 조직검사를 시행하는 당시의 정확한 판단에 근거한 조직검사 유도방식, 기구 선택, 적절한 술기에 의하여 상당 부분 결정되지만, 불충분한 또는 부정확한 검체 채취에 의한 위음성 또는 조직학적 저평가의 한계가 있을 수 있다. 이러한 이유로 영상-병리 합당성 판정을 포함한 조직검사 이후의 적절한 처치와 대응이 매우 중요하다. 조직검사 시 정확한 검체 획득이 이루어지지 않아 암 병변임에도 불구하고, 비특이적인 양성 병변의 병리 결과가 나오는 경우, 영상과 병리 간의 결과 일치 및 불일치 여부를 확인함으로써, 암을 놓치는 일을 막을 수 있다. 이 종설의 목적은 영상 유도하 유방 조직검사 후 결과의 정확한 해석을 위하여 구체적으로 고려할 사항들을 알아보고, 어떻게 적절한 평가를 할 수 있는지 알아보고자 한다.

• 한국전자파학회논문지
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• 제27권7호
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• pp.646-652
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• 2016

본 논문에서는 전자파를 이용한 유방암 진단에서 유방의 조직형태에 따른 2차원 영상복원 결과를 논의하고 있다. 유방의 영상복원에 사용한 시스템은 16개의 송신/수신 안테나로 구성되어 있으며, 1,700 MHz를 사용하여 4가지의 유방조직형태(ED-, HD-, SC-, FT-type)에 대하여 영상복원을 수행하였다. 순방향 문제의 해석에는 모멘트법을 적용하였으며, 역문제 해석을 위한 최적화 알고리즘은 simplex 법을 사용하였다. 영상복원의 결과, ED형 및 HD형은 영상복원이 용이하지만, SC형 및 FT형의 영상복원에는 오차가 많이 포함되어 있어 복원이 쉽지 않음을 확인할 수 있었다.

• 여성건강간호학회지
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• 제11권1호
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• pp.67-76
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• 2005

Purpose: This study was to identify the influence of sexual behavior, body image, social support, and other characteristics on sexual satisfaction in patients with breast cancer according to their participation in a support group. Method: Data was collected by self-report questionnaires. Participants included 63 patients attending a support group and 76 patients who did not participate in the support group. The questionnaire sections consisted of sexual satisfaction, sexual behavior, body image, social support and information on general characteristics, disease-related characteristics, and sexual life-related characteristics. Result: There was no statistically significant difference in sexual behavior, body image and sexual satisfaction between the two groups. Social support scores were significantly higher in the support group. Sexual satisfaction was positively related with sexual behavior, post-op change of sexual intercourse frequency, body image, and patient's education level, and negatively related to age in the support group. Sexual satisfaction was positively related with sexual behavior, social support and body image in the non support group. Sexual behavior is predictable 37.0% of sexual satisfaction in the support group. Sexual behavior, body image, and social support is predictable for 38.0% of the sexual satisfaction in non support group participants. Conclusion: Implications point to the need for the development and implementation of programs that focus specifically on sexual life issues for breast cancer patients, as well as further research measuring the effects of such intervention programs. Continuous education and counseling through participation in support groups can contribute to promote and affirm a healthy sexual life for patients with breast cancer.

• 종양간호연구
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• 제11권3호
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• pp.221-228
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• 2011

Purpose: This study was aimed to identify the influencing factors on the quality of life among breast cancer survivors. Methods: The subjects were 159 female patients who visited out-patient department (OPD) after the mass removal surgery for breast cancer and had completed adjuvant treatments such as chemotherapy, radiation therapy at a university hospital and a general hospital. Data collection was conducted using the Ferrell QOL scale, the Mishel uncertainty scale, the Fitts & Osgoods body image scale revised by Jeon & Kim. the Rosenberg self-esteem scale, and the Kang family support scale. Results: The level of QOL in the participants was in the middle. There were a significant correlation between QOL, uncertainty, self-esteem, and family support. There were significant differences in QOL with the perceived health condition and the best support person. In a regression analysis, the most powerful predictor of QOL was body image (21.7%). Altogether uncertainty and perceived health condition explained 28.6% of the variance of QOL of the participants. Conclusion: Body image, uncertainty, and perceived health condition were important predictors of QOL. These results demonstrated the need for developing interventions to improve QOL of breast cancer survivors.

• Asian Pacific Journal of Cancer Prevention
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• 제16권5호
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• pp.1795-1802
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• 2015

Background: Breast cancer is one of the most common cancer types in women and is amongst the most devastating and stressful events in the life of women. The external appearance of breast cancer patients usually changes due to the surgical and/or medical therapies used. An association may be found between social support perception and social appearance anxiety in patients with breast cancer in the period after mastectomy. Therefore, this study investigated the social appearance anxiety and social support status in women with breast cancer in our country. Materials and Methods: A descriptive cross-sectional study was conducted in breast cancer patients undergoing treatment or follow-up in Medical Oncology and General Surgery departments. Results: The mean age of the participants was $51.13{\pm}8.48$ years (range, 24-74 years) with nearly half of the patients (40.6%) aged 40-50 years. Of the patients, 39.1% had stage 3 breast cancer. The mean score on Cancer Patient's Social Support Scale (CPSSS) was $134.85{\pm}9.35$, and there was a significant difference in CPSSS total scores betweena the age groups, educational levels, self-reported income levels and stage of disease (p<0.05). The mean Social Image Anxiety Scale (SIAS) score was found to be $34.30{\pm}9.35$ (min:16, max:66) in women participating in this study. The CPSSS and SIAS scores of the participants were inversely correlated, and the SIAS score was found to decrease with the increasing CPSSS score but with no statistically significant difference (r=-0.110, p=0.217). Conclusions: Social appearance anxiety is higher in the patients with poor social support.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.134-136
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• 2021

인공지능 알고리즘을 이용한 유방암의 조기진단에 관련된 연구는 최근들어 활발하게 진행되고 있다. 이는 연구용으로 공개된 초음파 유방 이미지를 활용하여 다양하게 개발되고 있으나, 사용자의 목적에 맞는 처리 속도 및 정확도 등에 다양한 한계점을 보인다. 이러한 문제를 해결하기 위해, 본 논문에서는 ImageNet에서 학습된 ResNet 모델을 현미경 기반 암세포 이미지에서 활용이 가능한 다단계 전이 학습을 제안하고, 이를 다시 전이 학습하여 초음파 유방암 영상을 양성 및 악성으로 분류하는 실험을 진행하였다. 실험을 위한 영상은 양성과 악성이 포함된 250장의 유방암 초음파 영상과 27,200장의 암 세포주 영상으로 구성되었다. 제안된 다단계 전이 학습 알고리즘은 초음파 유방암 영상을 분류하였을 때 96% 이상의 정확도를 보였으며, 향후 암 세포주 및 실시간 영상처리 등의 추가를 통해 보다 높은 활용도와 정확도를 보일 것으로 기대한다.

• 산업공학
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• 제21권4호
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• pp.394-402
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• 2008

The objective of this paper is to design segmentation algorithm for applying the breast ultrasound image to CAD(Computer Aided Diagnosis). This study is conducted after understanding limits, used algorithm and demands of CAD system by interviewing with a medical doctor and analyzing related works based on a general CAD framework that is consisted of five step-establishment of plan, analysis of needs, design, implementation and test & maintenance. Detection function of CAD is accomplished by Canny algorithm and arithmetic operations for segmentation. In addition to, long computing time is solved by extracting ROI (Region Of Interests) and applying segmentation technical methods based morphology algorithm. Overall course of study is conducted by verification of medical doctor. And validity and verification are satisfied by medical doctor's confirmation. Moreover, manual segmentation of related works, restrictions on the number of tumor and dependency of image resolution etc. was solved. This study is utilized as a support system aided doctors' subjective diagnosis even though a lot of future studies is needed for entire application of CAD system.

• Asian Pacific Journal of Cancer Prevention
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• 제16권14호
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• pp.5613-5618
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• 2015

Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

• 한국생산제조학회지
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• 제22권3_1spc호
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• pp.496-503
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• 2013

Recently, the death rate owing to breast cancers has been increasing, and the occurrence age for breast cancers is lowering every year. Mammography is known to be a reliable detection method for breast cancers and works by detecting texture changes, calcifications, and other potential symptoms. In this research on breast cancer detection, candidate objects were detected by using image processing on mammograms, and feature analysis was used to classify candidate objects as benign tumors and malignant tumors. To find candidate objects, image pre-processing and binarization using multiple thresholds, and the grouping of micro-calcifications were used. More than 50 shape features and intensity features were used in the classification. The performance of the detection algorithm by using Euclidian distance method for benign tumors was 93%, and the classification error rate was approximately 2%.