• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.8
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• pp.1272-1277
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• 2017

To assist radiologists for the characterization of breast masses, Computer-aided Diagnosis(CADx) system has been studied. The CADx system can improve the diagnostic accuracy of radiologists by providing objective information about breast masses. Morphological and texture features were extracted from the breast ultrasound images. Based on extracted features, the CADx system retrieves masses that are similar to a query mass from a reference library using a k-nearest neighbor (k-NN) approach. Eight similarity measures of distance, Euclidean, Chebyshev(Minkowski family), Canberra, Lorentzian($F_2$ family), Wave Hedges, Motyka(Intersection family), and Cosine, Dice(Inner Product family) are evaluated by ROC(Receiver Operating Characteristic) analysis. The Inner Product family measure used with the k-NN classifier provided slightly higher performance for classification of malignant and benign masses than those with the Minkowski, $F_2$, and Intersection family measures.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• The Korean Journal of Nuclear Medicine
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• v.36 no.3
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• pp.177-184
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• 2002

Purpose: Ultrasonography and $^{99m}-Tc-MIBI$ scintimammography were validated as useful diagnostic tools for primary breast cancer. However, ultrasound has the problem of low specificity. We compared the diagnostic usefulness of ultrasound with $^{99m}-Tc-MIBI$ scintimammography in the diagnosis of breast cancer. Materials and Methods: This study included 174 patients who had ultrasound and $^{99m}-Tc-MIBI$ scintimammography peformed on breast masses from 1999 to 2000. The pathologic results were obtained by surgery or FNAB. Results: Among the 174 patients, malignant breast disease numbered 117 and benign breast disease numbered 57. Ultrasound revealed 88 TP, 9 FN, 8 FP, 34 TN, and 35 indeterminate cases. $^{99m}-Tc-MIBI$ scintimammography revealed 91 TP, 25 FN, 9 FP, and 48 TN. The sensitivity, specificity, positive predictive value, and negative predictive value of Ultrasound were 66.7%, 44.2%, 67.2%, and 43.6% respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of $^{99m}-Tc-MIBI$ scintimammography were 77.8%, 84.2%, 91%, and 64.9% respectively. Among the 35 indeterminate ultrasound cases, $^{99m}-Tc-MIBI$ scintimammography revealed 13 TP, 15 TN, and 7 FP Conclusion: $^{99m}-Tc-MIBI$ Scintimammography was more sensitive and specific than ultrasound for the detection of primary breast cancer and provided more useful information in cases of indeterminate ultrasound findings.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.61-69
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• 2008

Diagnosis of breast ultrasound is better than mammography in the early detection of breast cancer, but, it is difficult to detect microcalcification. We studied on detection for calcification of breast tissue using acoustic resonance and power doppler with 7.5 MHz linear probe in breast ultrasound. We first constructed breast tissue phantom made of gelatin and saw breast, and then observed calcification by the change of external vibration. Calcification injected breast tissue phantom visualized the difference for brightness and region of color in ROI regions of power doppler. Acoustic resonance almost never visualized in low frequency regions, plateau constituted in about 300-400 Hz and colors vanished according to the increase of frequency.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.74-82
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• 2008

The objective of this paper is to remove noises of image based on the heuristic noises filter and to extract a tumor region by using morphology techniques in breast ultrasound image. Similar objective studies have been conducted based on ultrasound image of high resolution. As a result, efficiency of noise removal is not fine enough for low resolution image. Moreover, when ultrasound image has multiple tumors, the extraction of ROI (Region Of Interest) is not accomplished or processed by a manual selection. In this paper, our method is done 4 kinds of process for noises removal and the extraction of ROI for solving problems of restrictive automated segmentation. First process is that pixel value is acquired as matrix type. Second process is a image preprocessing phase that is aimed to maximize a contrast of image and prevent a leak of personal information. In next process, the heuristic noise filter that is based on opinion of medical specialist is applied to remove noises. The last process is to extract a tumor region by using morphology techniques. As a result, the noise is effectively eliminated in all images and a extraction of tumor regions is possible though one ultrasound image has several tumors.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.274-283
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• 2023

Objective: To compare the outcomes of digital breast tomosynthesis (DBT) screening combined with ultrasound (US) with those of digital mammography (DM) combined with US in women with dense breasts. Materials and Methods: A retrospective database search identified consecutive asymptomatic women with dense breasts who underwent breast cancer screening with DBT or DM and whole-breast US simultaneously between June 2016 and July 2019. Women who underwent DBT + US (DBT cohort) and DM + US (DM cohort) were matched using 1:2 ratio according to mammographic density, age, menopausal status, hormone replacement therapy, and a family history of breast cancer. The cancer detection rate (CDR) per 1000 screening examinations, abnormal interpretation rate (AIR), sensitivity, and specificity were compared. Results: A total of 863 women in the DBT cohort were matched with 1726 women in the DM cohort (median age, 53 years; interquartile range, 40-78 years) and 26 breast cancers (9 in the DBT cohort and 17 in the DM cohort) were identified. The DBT and DM cohorts showed comparable CDR (10.4 [9 of 863; 95% confidence interval {CI}: 4.8-19.7] vs. 9.8 [17 of 1726; 95% CI: 5.7-15.7] per 1000 examinations, respectively; P = 0.889). DBT cohort showed a higher AIR than the DM cohort (31.6% [273 of 863; 95% CI: 28.5%-34.9%] vs. 22.4% [387 of 1726; 95% CI: 20.5%-24.5%]; P < 0.001). The sensitivity for both cohorts was 100%. In women with negative findings on DBT or DM, supplemental US yielded similar CDRs in both DBT and DM cohorts (4.0 vs. 3.3 per 1000 examinations, respectively; P = 0.803) and higher AIR in the DBT cohort (24.8% [188 of 758; 95% CI: 21.8%-28.0%] vs. 16.9% [257 of 1516; 95% CI: 15.1%-18.9%; P < 0.001). Conclusion: DBT screening combined with US showed comparable CDR but lower specificity than DM screening combined with US in women with dense breasts.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.213-220
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• 2015

This research was study the accuracy of three-dimensional stereotactic breast biopsy, using a core Needle Biopsy and to assess the accuracy of Stereotactic biopsy and Sono guided biopsy. Using Stereotactic QC phantom to measure the accuracy of the 3D sterotactic machine. CT Scan and equipment obtained in the measured X, Y, Z and compares the accuracy of the length. Using Agar power phantom compare the accuracy of the 3D sterotactic machine and 2D ultrasound machine. Z axis measured by the equipment to compare the accuracy and reliability. Check the accuracy by using visual inspection and Specimen Medical application phantom. The accuracy of the 3D sterotactic machine measured by Stereotactic QC phantom was 100%. Accuracy as compared to CT, all of X, Y, Z axis is p > 0.05. The accuracy of the two devices was 100% as measured by Agar powder phantom. There was no difference between t he t wo d evices as C T and p > 0.05. 3D sterotactic machine of the ICC was 0.954, 2D ultrasound machine was 0.785. 2D ultrasound machine was different according to the inspector. Medical application phantom experiments in 3D sterotactic machine could not find the Sliced boneless ham. 2D ultrasound machine has not been able to find a small chalk powder group. The reproducibility of the three-dimensional stereotactic breast biopsy was better than effect of Sono guided biopsy.

• 전기의세계
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• v.66 no.8
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• pp.33-35
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• 2017

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.135-149
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• 2022

Purpose: To assess clinically significant imaging findings of malignant intramammary lymph nodes (IMLNs) in breast cancer patients and to evaluate their diagnostic performance in predicting malignant IMLN. Materials and Methods: A total of 110 cases with IMLN of BI-RADS category 3 or more, not typical benign IMLN, in MR of breast cancer patients between January 2016 and January 2021 were retrospectively reviewed. After excluding 33 cases, 77 cases were finally included. Among them, 58 and 19 were confirmed as benign and malignant, respectively. Qualitative and quantitative MR imaging features of the IMLN were retrospectively analyzed. Sizes and final assessment categories of IMLN on MRI, mammography, and ultrasound were reviewed. Diagnostic performances of imaging features on MRI, mammography, and ultrasound were then evaluated. Results: For qualitative MR features, shape, margin, and preserved central hilum were significantly different between benign and malignant groups (P < 0.05). For quantitative MR features, long diameter over 6 mm, short diameter over 4 mm, and cortical thickening over 3 mm showed high sensitivities in predicting malignant IMLNs (89.5%, 94.7%, and 100%, respectively). Size exceeding 1 cm showed high specificity and accuracy in predicting malignant IMLN on MR, mammography, and ultrasound (91.4% and 80.5%; 96.6% and 79.25; 98.3% and 80.5%, respectively). Conclusion: Various MR imaging features and size can be helpful for predicting malignant IMLN in breast cancer patients.