• Journal of radiological science and technology
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• v.45 no.2
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• pp.103-109
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• 2022

To maintain improved image quality in mammography, the quality control process is performed using the ACR (American college of radiology) phantom. In addition, many studied were performed by fabricating the customized breast phantom to provide more information in mammography. Thus, the purpose of this study was to evaluate the image quality by designing the modified ACR phantoms. The five modified acrlylic ACR phantoms were designed by considering insert position and phantom thickness. The phantoms were consisted of 4.5, 3.0, and 1.5 cm in terms of phantom thickness, and 3.0, 2.0, and 0.5 cm in terms of insert position, respectively. The acquired images were evaluated by PSNR (peak signal to noise ratio), RMSE (root mean square error), CC (correlation coefficient), CNR (contrast to noise ratio), and COV (coefficient of variation). Based on the similarity analysis, the result is suitable between conventional and new designed phantoms. In addition, the CNR and COV results in terms of insert position showed that image quality for 0.5 cm was 2.3 and 27.4% improved compared with 2 and 3 cm, respectively. According to phantom thickness results, the CNR result for 1.5 cm and COV result for 4.5 cm were 50.1 and 62.7% improved compared with that those conditions. In conclusion, we confirmed that the image quality depends on the breast size and thickness through modified ACR phantom study.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.45-54
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• 2009

In this paper, we propose an adult images detection algorithm using a mean intensity filter and an improved 2D Hough Transform. This paper is composed of three major steps including a training step, a recognition step, and a verification step. The training step generates a mean nipple variance filter that will be used for detecting nipple candidate regions in the recognition step. To make the mean variance filter, we converts an input color image into a gray scale image and normalize it, and make an average intensity filter for nipple areas. The recognition step first extracts edge images and finds connected components, and decides nipple candidate regions by considering the ratio of width and height of a connected component. It then decides final nipple candidates by calculating the similarity between the learned nipple average intensity filter and the nipple candidate areas. Also, it detects breast lines of an input image through the improved 2D Hough transform. The verification step detects breast areas and identifies adult images by considering the relations between nipple candidate regions and locations of breast lines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.860-880
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• 2024

Breast cancer is a major health concern affecting women and men globally. Early detection and accurate classification of breast cancer are vital for effective treatment and survival of patients. This study addresses the challenge of accurately classifying breast tumors using machine learning classifiers such as MLP, AdaBoostM1, logit Boost, Bayes Net, and the J48 decision tree. The research uses a dataset available publicly on GitHub to assess the classifiers' performance and differentiate between the occurrence and non-occurrence of breast cancer. The study compares the 10-fold and 5-fold cross-validation effectiveness, showing that 10-fold cross-validation provides superior results. Also, it examines the impact of varying split percentages, with a 66% split yielding the best performance. This shows the importance of selecting appropriate validation techniques for machine learning-based breast tumor classification. The results also indicate that the J48 decision tree method is the most accurate classifier, providing valuable insights for developing predictive models for cancer diagnosis and advancing computational medical research.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.751-759
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• 2023

Breast ultrasonography is difficult to image in fatty breasts and to find micro-calcification, but the discovery of micro-calcification is very important for breast cancer screening. Among the color Doppler artifact of ultrasound, twinkle artifact mainly occur on strong reflectors such as stones or calcification in images, and evaluation methods using them are clinically being used. In this study, we are conducting experiments on the color Doppler settings of ultrasound equipment, such as repetition frequency, ensemble, persist, wall filtering, smoothing, linear density, and dissociation value, by producing a breast simulation phantom using the largest amount of calcium phosphate among breast implants. The purpose of this study was to improve the contrast of twinkle artifact in breast ultrasound examinations and to maximize their use in clinical practice. As a result, the pulse repetition frequency occurred in the range of 3.6 kHz to 7.2 kHz, and did not occur above 10.5 kHz. For ensembles, twinkle artifact occurred in all sizes of calcification under low conditions, and in threshold settings, the twinkle artifact increased slightly only under 80 to 100 conditions, and did not occur in 1 mm size calcification. Persist, wall filter, smoothing, and line density settings did not have much meaning in the setting variable because conditions did not increase by condition, and pulse repetition frequency, ensemble, and thresholds had the greatest impact on the twinkling artifact image. This study is expected to help examiners select optimal conditions to effectively increase twinkle artifact by adjusting color Doppler settings.

• Journal of the Korean Society of Radiology
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• v.82 no.6
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• pp.1477-1492
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• 2021

Purpose Dedicated breast CT is an emerging volumetric X-ray imaging modality for diagnosis that does not require any painful breast compression. To improve the detection rate of weakly enhanced lesions, an adaptive image rescaling (AIR) technique was proposed. Materials and Methods Two disks containing five identical holes and five holes of different diameters were scanned using 60/100 kVp to obtain single-energy CT (SECT), dual-energy CT (DECT), and AIR images. A piece of pork was also scanned as a subclinical trial. The image quality was evaluated using image contrast and contrast-to-noise ratio (CNR). The difference of imaging performances was confirmed using student's t test. Results Total mean image contrast of AIR (0.70) reached 74.5% of that of DECT (0.94) and was higher than that of SECT (0.22) by 318.2%. Total mean CNR of AIR (5.08) was 35.5% of that of SECT (14.30) and was higher than that of DECT (2.28) by 222.8%. A similar trend was observed in the subclinical study. Conclusion The results demonstrated superior image contrast of AIR over SECT, and its higher overall image quality compared to DECT with half the exposure. Therefore, AIR seems to have the potential to improve the detectability of lesions with dedicated breast CT.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.365-368
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• 1997

We are developing a small gamma camera or imaging malignant breast tumors. The small scintillation camera system consists of NaI(Tl) crystal ($60\;{\times}\;60\;{\times}\;6\;mm^3$) coupled to position sensitive photomultiplier tube (PSPMT), nuclear instrument module (NIM), analog to digital converter (ADC), and personal computer. High quality flood source image and hole mask image were obtained using the gamma camera developed in this study. Breast phantom containing $2{\sim}7\;mm$ diameter spheres was successfully imaged with parallel hole collimator. The obtained image displayed accurate activity distribution over the imaging field of view. Linearity and uniformity correction algorithms are being developed. It is believed that the developed small gamma camera could be useful or detection of malignant 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.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.11
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• pp.4685-4690
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• 2015

Purpose: To evaluate the correlation between cosmetic outcome (CO), body image, and quality of life in post breast-conserving therapy (BCT) women. Materials and Methods: This cross-sectional study concerned one-year post-completed BCT Thai women. The data included subjective and objective CO with a questionnaire covering demographic and clinical data, anti-hormonal treatment status, Eastern Cooperative Oncology Group (ECOG) performance status, Self-Reported Cosmetic Outcomes (SRCO), Self-Reported Breast Symmetry (SRBS), Body Image Scale (BIS), and the Functional Assessment of Cancer Therapy with Breast Cancer subscale (FACT-B). Participants had breast photographs taken for the evaluation of objective cosmetic outcome (OCO) after breast cancer conservation treatment. The relationship between CO and FACT-B was tested using Spearman's rank correlation Results: A total 127 participants volunteered for the study. The participant characteristics were age 52(${\pm}9$), Buddhist 87%, married 65%, body mass index 25.0(${\pm}4.6$), breast cup size A-C 91%, college educated 60%, employed 66%, ECOG 0-1 95%, tumor size less than or equal to 2 cm 55%, no lymph node metastasis 98%, and taking tamoxifen 57%. Two percent of the participants regretted their decision to undergo BCT. The SRCO was excellent in 2%, good in 68%, fair in 30%, and poor in 0%. For SRBS, rates were 17%, 58%, 24% and 1% for excellent, good, fair and poor cosmetic outcomes, respectively. The BCCT scores were excellent 24%, good 39%, fair 32%, and poor 6%. The median total QOL score of the participants was 130 (93-144). There was no significant correlation between CO and FACT-B scores. Conclusions: The significance of CO for FACT-B in Thai women with breast cancer could not be assessed in detail because of a very low level of correlation. The results may be due to the effects of cultural background.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2241-2247
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• 2012

Objective: This qualitative study retrospectively examined the experience and psychological impact of contralateral prophylactic mastectomy (CPM) among Southern Chinese females with unilateral breast cancer history who underwent BRCA1/2 genetic testing. Limited knowledge is available on this topic especially among Asians; therefore, the aim of this study was to acquire insight from Chinese females' subjective perspectives. Methods: A total of 12 semi-structured in-depth interviews, with 11 female BRCA1/BRCA 2 mutated gene carriers and 1 non-carrier with a history of one-sided breast cancer and genetic testing performed by the Hong Kong Hereditary Breast Cancer Family Registry, who subsequently underwent CPM, were assessed using thematic analysis and a Stage Conceptual Model. Breast cancer history, procedures conducted, cosmetic satisfaction, pain, body image and sexuality issues, and cancer risk perception were discussed. Retrieval of medical records using a prospective database was also performed. Results: All participants opted for prophylaxis due to their reservations concerning the efficacy of surveillance and worries of recurrent breast cancer risk. Most participants were satisfied with the overall results and their decision. One-fourth expressed different extents of regrets. Psychological relief and decreased breast cancer risk were stated as major benefits. Spouses' reactions and support were crucial for post-surgery sexual satisfaction and long-term adjustment. Conclusions: Our findings indicate that thorough education on cancer risk and realistic expectations of surgery outcomes are crucial for positive adjustment after CPM. Appropriate genetic counseling and pre-and post-surgery psychological counseling were necessary. This study adds valuable contextual insights into the experiences of living with breast cancer fear and the importance of involving spouses when counseling these patients.

• Journal of radiological science and technology
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• v.42 no.2
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• pp.137-143
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• 2019

The aim of this study was analyzed the setup error of breast cancer patients in intensity modulated radiation therapy(IMRT) with deep inspiration breath holding(DIBH) and was analyzed the dose distribution due to setup error. A total of 45 breast cancer cases were performed a retrospective clinical analysis of setup error. In addition, the re-treatment planning was carried by shifting the setup error from the isocenter at the treatment. Based on this, the dose distribution of PTV and OARs was compared and analyzed. The 3D error for small breast group and medium breast group and large breast group were 3.1 mm and 3.7 mm and 4.1 mm, respectively. The difference between the groups was statistically significant(P=0.003). DVH results showed HI, CI for the PTV difference between standard treatment plan and re-treatment plan of 14.4%, 4%. The difference in $D_5$ and $V_{20}$ of the ipsilateral lung was 5.6%, 13% respectively. The difference in $D_5$ and $V_5$ of the heart of right breast cancer patients was 6.8%, 8% respectively. The difference in $D_5$, $V_{20}$ of the heart of left breast cancer patients was 7.2%, 23.5% respectively. In this study, there was a significant association between breast size and significant setup error in breast cancer patients with DIBH. In addition, it was found that the dose distribution of the PTV and OARs varied according to the setup error.