• Journal of the Korean Society of Radiology
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• v.83 no.5
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• pp.1090-1103
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• 2022

Purpose To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) with the two-dimensional synthesized mammogram (2DSM), compared to full-field digital mammography (FFDM), for suspicious microcalcifications in the breast ahead of stereotactic biopsy and to assess the diagnostic image visibility of the images. Materials and Methods This retrospective study involved 189 patients with microcalcifications, which were histopathologically verified by stereotactic breast biopsy, who underwent DBT with 2DSM and FFDM between January 8, 2015, and January 20, 2020. Two radiologists assessed all cases of microcalcifications based on Breast Imaging Reporting and Data System (BI-RADS) independently. They were blinded to the histopathologic outcome and additionally evaluated lesion visibility using a fivepoint scoring scale. Results Overall, the inter-observer agreement was excellent (0.9559). Under the setting of category 4A as negative due to the low possibility of malignancy and to avoid the dilution of malignancy criteria in our study, McNemar tests confirmed no significant difference between the performances of the two modalities in detecting microcalcifications with a high potential for malignancy (4B, 4C, or 5; p = 0.1573); however, the tests showed a significant difference between their performances in detecting microcalcifications with a high potential for benignancy (4A; p = 0.0009). DBT with 2DSM demonstrated superior visibility and diagnostic performance than FFDM in dense breasts. Conclusion DBT with 2DSM is superior to FFDM in terms of total diagnostic accuracy and lesion visibility for benign microcalcifications in dense breasts. This study suggests a promising role for DBT with 2DSM as an accommodating tool for stereotactic biopsy in female with dense breasts and suspicious breast microcalcifications.

• The Korean Journal of Nuclear Medicine
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• v.34 no.5
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• pp.393-402
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• 2000

Purpose: Tc-99m-MIBI (MIBI) and Tc-99m-Tetrofosmin (TF) are commonly used for scintimammog (SMM). We compared the diagnostic ability of SMM using Tc-99m-MIBI and Tc-99m-TF for the diagnosis of breast mass. Materials and Methods: The study subjects were comprised of 123 breast lesior 86 normal breasts of 114 patients who underwent SMM. Bilateral prone images and anterior supine images obtained at 5 minutes and 1 or 3 hours after intravenous injection of 740 MBq of either MIBI or TF. of tumors were not significantly different between the MIBI and TF groups. First, two observers read the SMM without clinical information (1st interpretation), then read again with information about location (2nd interpretation). Sensitivity and specificity of each radiopharmaceutical for the diagnosis of cancer were evaluated in terms of image acquisition time, tumor size, and location. Results: The SMM a good agreement between two observers for 1st and 2nd interpretation, except for TF SMM at 3 hr. first interpretation, the sensitivities at 5 min, 1 hr, and 3 hr were not significantly different between MIBI TF SMM (81.6%, 80.0%, 60.9% in MIBI vs. 88.9%, 80.6%, 42.9% in TF), although the sensitivities of images were significantly lower than 5 min images in both MIBI and TF SMM. The specificity of TF at was superior to that of MIBI (81.5%, 90.0%, 82.9% in MIBI vs. 96.7%, 100%, 90.0% in TF, p<0.01 MIBI TF at 5 min). For the second interpretation with information of mass location, the sensitivities at 3 hr were significantly lower than 5 min images (86.8%, 86.7%, 78.3% in MIBI vs. 88.9%, 93.5%, 57.1% between MIBI and TF SMM. However, there was no significant difference in the specificity (60.0%, 75.0% for MIBI vs. 86.7%, 100%, 100% for TF). MIBI and TF SMM showed lower sensitivities for the with less than 1 cm than tumors with more than 1 cm. However, the location of tumors did not sensitivity and specificity between MIBI and TF SMM. Conclusion: The ability for the differential of breast tumor is similar between MIBI and TF SMM, and delayed image is not necessary. TF may be than MIBI considering the specificity of SMM without clinical information and labeling convenience.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.482-491
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• 2005

In this paper, we proposed the microcalcification detection algorithm which is based on wavelet transform and automatic thresholding method in the X-ray mammographic images. Digital X-ray imaging system is essential equipment in the field diagnosis and is widely used in the various fields such as chest, fracture of a bone, and dental correction. Especially, digital X-ray mammographic imaging is known as the most important method to diagnose the breast cancer, many researches to develop the imaging system are processing in country. In this paper, we proposed a microcalcifications detection algorithm necessary in the early phase of breast cancer diagnosis and showed that a algorithm could effectively detect microcalfication and could aid diagnosis-radiologist.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.108-115
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• 2001

In this paper we are attempted to quantitative classification of the three object color regions on a RGB image using of an improved ML(Maximum Likelihood) classification method. A RGB color image consists of three bands i.e., red, green and blue. Therefore it has a 3 dimensional structure in view of the spectral and spatial elements. The 3D structural yokels were projected in RGB cube wherefrom the ML method applied. Between the conventionally and easily usable Box classification and the statistical ML classification based on Bayesian decision theory, we compared and reviewed. Using the ML method we obtained a good segmentation result to classify positive cell nucleus, negative cell Nucleus and background un a immuno-histological breast carcinoma image. Hopefully it is available to diagnosis and prognosis for cancer patients.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.471-476
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• 2007

Various active microwave imaging techniques have been developed for cancer detection for past several decades. Both the microwave tomography and the UWB radar techniques, constituting functional microwave imaging systems, use the electrical property contrast between normal tissues and malignancies to detect the latter in an early development stage. Even though promising simulation results have been reported, the understanding of the functional microwave imaging diagnostics has been relied heavily on the complicated numerical results. We present a computationally efficient and physically instructive analytical electromagnetic wave channel models developed for functional microwave imaging system in order to detect especially the breast tumors as early as possible. The channel model covers the propagation factors that have been examined in the previous 2-D models, such as the radial spreading, path loss, partial reflection and transmission of the backscattered electromagnetic waves from the tumor cell. The effects of the system noise and the noise from the inhomogeneity of the tissue to the reconstruction algorithm are modeled as well. The characteristics of the reconstructed images of the tumor using the proposed model are compared with those from the confocal microwave imaging.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• Progress in Medical Physics
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• v.32 no.4
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• pp.159-164
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• 2021

Purpose: We investigated the properties of CLEANBOLUS based on silicone with suitable characteristics for clinical use. Methods: We evaluated the characteristics of CLEANBOLUS and compared the results with the commercial product (Super-Flex bolus). Also, we conducted physical evaluations, including shore hardness, element composition, and elongation break. Transparency was investigated through the measured absorbance within the visible region (400-700 nm). Also, dosimetric characteristics were investigated with surface dose and beam quality. Finally, the volume of unwanted air gap was investigated based on computed tomography images for breast, chin, and nose using Super-Flex bolus and CELANBOLUS. Results: CLEANBOLUS showed excellent physical properties for a low shore hardness (000-35) and elongation break (>1,000%). Additionally, it was shown that CLEANBOLUS is more transparent than Super-Flex bolus. Dosimetric results obtained through measurement and calculation have an electron density similar to water in CLEANBOLUS. Finally, CLEANBOLUS showed that the volume of unwanted air gap between the phantom and each bolus is smaller than Super-Flex bolus for breast, chin, and nose. Conclusions: The physical properties of CLEANBOLUS, including excellent adhesive strength and lower shore hardness, reduce unwanted air gaps and ensure accurate dose distribution. Therefore, it would be an alternative to other boluses, thus improving clinical use efficiency.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1383-1388
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• 2012

Objectives: Regardless of its negative impact on quality of life, little is known about the importance of alopecia from the patients' perspective. This study aimed to explore the whole experience of chemotherapy-induced alopecia among Korean breast cancer patients including perception, attitudes, preparedness, and changes after alopecia. Methods: Patients expected to experience or had experienced alopecia were recruited at a tertiary hospital in Seoul, Korea. Semi-structured in-depth interviews were performed in 21 patients. Recurrent issues were identified and placed into thematic categories. Results: All patients think that appearance is important and they pay attention to how they look like. They had negative perceptions about alopecia. Patients were not well prepared for alopecia, and experienced substantial physical, psychological and social distress. Lack of information and limited social support combined with negative images of cancer made it difficult for patients to overcome the trauma and deterred them from usual daily activities resulting in poor quality of life. Conclusions: Patients were not well prepared for alopecia and negative perceptions, lack of preparedness, and limited social support and resources increased alopecia-related distress. Educational programs for preparing patients to cope with alopecia distress and advocate activities to change people's negative perception about alopecia are needed to reduce the burden imposed by alopecia in cancer patients.