• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.114-122
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• 2010

This paper presents a method that registers a lymphoscintigraphy to the real image captured by a CMOS camera, which helps surgeons to easily and precisely detect sentinel lymph nodes for sentinel lymph node biopsy in breast cancer patients. The proposed method consists of two steps: pre-matching and image registration. In the first step, we localize fiducial markers in a lymphoscintigraphy and a real image of a four quadrant bar phantom by using image processing techniques, and then determines perspective transformation parameters by matching with the corresponding marker points. In the second step, we register a lymphoscintigraphy to a real images of patients by using the perspective transformation of pre-matching. To examine the accuracy of the proposed method, we conducted an experiment with a chest mock-up with radioactive markers. As a result, the euclidean distance between corresponding markers was less than 3mm. In conclusion, the present method can be used to accurately align lymphoscintigraphy and real images of patients without attached markers to patients, and then provide useful anatomical information on sentinel lymph node biopsy.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.38-41
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• 2002

Mammography is considered the single most important diagnostic tool in the early detection of breast cancer. Today's dedicated mammographic equipment, specially designed x-ray screen/film combinations, coupled with controlled film processing, produces excellent image quality and can detect very low contrast small lesions. In mammography, it is most important to produce consistent high-contrast, high-resolution images at the lowest radiation dose consistent with high image quality. Some of the major technical development milestones that have let to today's high quality in mammographic imaging are reviewed. Both the American College of Radiology Mammography Accreditation Program and the Mammography Quality Standards Act have significant impact on the improvement of the technical quality of mammographic images in the United States and worldwide. A most recent development in digital mammography has opened up avenues for improving diagnosis.

• Investigative Magnetic Resonance Imaging
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• v.22 no.3
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• pp.194-199
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• 2018

We present image findings, especially rare MRI of a primary breast angiosarcoma with its histopathology, and also analyze the relevant medical literature reports in terms of the MRI findings. As our patient had unique features of a primary breast angiosarcoma, this case could be very helpful for future diagnosis of this rare breast malignancy by MRI.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.124-131
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• 2024

Breast cancer classification using ultrasound, while widely employed, faces challenges due to its relatively low predictive value arising from significant overlap in characteristics between benign and malignant lesions, as well as operator-dependency. To alleviate these challenges and reduce dependency on radiologist interpretation, the implementation of automatic breast cancer classification in ultrasound image can be helpful. To deal with this problem, we propose a semi-supervised deep learning framework for breast cancer classification. In the proposed method, we could achieve reasonable performance utilizing less than 50% of the training data for supervised learning in comparison to when we utilized a 100% labeled dataset for training. Though it requires more modification, this methodology may be able to alleviate the time-consuming annotation burden on radiologists by reducing the number of annotation, contributing to a more efficient and effective breast cancer detection process in ultrasound images.

• Applied Microscopy
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• v.51
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• pp.6.1-6.9
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• 2021

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.107-114
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• 2012

Microwave imaging (MI) is one of the most promising and attractive new techniques for earlier breast cancer detection. Microwave tomography (MT) realizes configuration of a multistatic multiple-input multiple-output system and reconstructs dielectric properties of the breast by solving a nonlinear inversion scattering problem. In this paper, we describe ETRI 3D MT system with 3D MI reconstruction program and demonstrate its robustness through some examples of the image reconstruction.

• Journal of the Ergonomics Society of Korea
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• v.33 no.4
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• pp.299-311
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• 2014

Objective: The aim of this study was to develop a method for measuring breast size from three-dimensional (3D) body scan image data. Background: Previous bra studies established reference points by directly contacting the subject's naked skin to determine the boundary of the breast. But some subjects were uncomfortable with these types of measurements. This study examined noncontact methods of extracting breast reference points from 3D body scan data that were collected while subjects were wearing standardized soft bras. Method: 3D body scan data of 32 Korean women were analyzed. The subjects were selected from the Size Korea 2010 study. The breast landmarks were identified by graphic analyses of slicing contour lines on 3D body scan data. Results: Three methods determining bra cup size were compared. The M1 and M2 methods determined cup size by calculating the difference between bust girth and under-bust girth. The M3 method determined bra cup size by measuring breast arc length. Conclusion: The researchers proposed an anthropometric bra cup sizing system with the breast arc length (M3 method). It was measured from the geometrically defined landmarks on the 3D body scan slicing contour lines. The new bra cup size was highly correlated with breast depth. Application: The noncontact measuring method used in this study can be applied to the ergonomic studies measuring sensitive body parts.

• 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.

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.83-95
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• 2022

The purpose of this study was to establish and provide guidelines for the standardized acquisition and interpretation of diffusion-weighted magnetic resonance imaging (DW-MRI) to improve the image quality and reduce the variability of the results interpretation. The standardized protocol includes the use of high-resolution DW-MRI with advanced techniques and post-processing. The aim of the protocol is to increase the effectiveness of the medical image information exchange involved in the construction, activation, and exchange of clinical information for healthcare use. An organized interpretation form could make DW-MRIs' interpretation easier and more familiar. Herein, the authors briefly review the basic principles, optimized image acquisition, standardized interpretation guidelines, false negative and false positive cases of DW-MRI, and provide a standard interpretation form and examples of various cases to help users become more familiar with the DW-MRI.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.307-310
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• 2021

In the recent years, studies using Computer-Aided Diagnostics(CAD) have been actively conducted, such as signal and image processing technology using breast ultrasound images, automatic image optimization technology, and automatic detection and classification of breast masses. As computer diagnostic technology is developed, it is expected that early detection of cancer will proceed accurately and quickly, reducing health insurance and test ice for patients, and eliminating anxiety about biopsy. In this paper, a quantitative analysis of tumors was conducted in ultrasound images using a gray level co-occurrence matrix(GLCM) to experiment with the possibility of use for computer assistance diagnosis.