• Korean Journal of Digital Imaging in Medicine
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• v.16 no.2
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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

• Journal of the Korean Society of Radiology
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• v.84 no.2
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• pp.361-371
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• 2023

The success of image-guided breast biopsy depends on the biopsy method, needle selection, and appropriate technique based on the accurate judgment by the radiologist at biopsy. However, insufficient or inappropriate sampling of specimens may result in false-negative results or pathologic underestimation. Therefore, image-pathology concordance assessments after biopsy are essential for appropriate patient management. Particularly, the assessment of image-pathology concordance can avoid false-negative reports of breast cancer as a benign pathology. Therefore, this study aimed to discuss factors that impact the accurate interpretation of image-guided breast biopsy along with the appropriate assessments.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.7
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• pp.646-652
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• 2016

This paper presents a tumor detection for breast cancer that utilizes two-dimensional(2D) image reconstruction with microwave tomographic imaging. The breast cancer detection system under development consists of 16 transmit/receive antennas, and the microwave tomography system operates at 1,700 MHz. The four types of breast(ED-, HD-, SC-, and FT-type) are used for image reconstruction. To solve a 2D inverse scattering problem, the method of moments(MoM) is employed for forward problem solving, and the simplex method employed as an optimization algorithm. The results of the reconstructed image show that the ED- and HD-types of breasts are well reconstructed, but SC- and FT-type breasts are not well because of the error including.

• Women's Health Nursing
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• v.11 no.1
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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.

• Asian Oncology Nursing
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• v.11 no.3
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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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• v.16 no.5
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.134-136
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• 2021

Research related to early diagnosis of breast cancer using artificial intelligence algorithms has been actively conducted in recent years. Although various algorithms that classify breast cancer based on a few publicly available ultrasound breast cancer images have been published, these methods show various limitations such as, processing speed and accuracy suitable for the user's purpose. To solve this problem, in this paper, we propose a multi-stage transfer learning where ResNet model trained on ImageNet is transfer learned to microscopic cancer cell line images, which was again transfer learned to classify ultrasound breast cancer images as benign and malignant. The images for the experiment consisted of 250 breast cancer ultrasound images including benign and malignant images and 27,200 cancer cell line images. The proposed multi-stage transfer learning algorithm showed more than 96% accuracy when classifying ultrasound breast cancer images, and is expected to show higher utilization and accuracy through the addition of more cancer cell lines and real-time image processing in the future.

• IE interfaces
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• v.21 no.4
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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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• v.16 no.14
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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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%.