• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.123-126
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• 1995

The purpose of this paper is to improve ultrasound images using motion estimation and recursive filtering. Although averaging without motion correction can make image blurring, the proposed estimation method improves image SNR without motion blurring by recursively averaging images with motion correction. Computer simulation on the proposed method has been performed to improve phantom and ultrasound fish images and the results show the utility of the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.70-71
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• 1998

In this paper, We proposed fast ultrasound image compression based on characteristics of ultrasound images. In the proposed method, wavelet transform is performed for non-zero coefficients selectively. It codes zero-tree symbols using conditional pdf (probability density function) as orientation of bands. It normalizes wavelet coefficients with threshold of each wavelet band and encodes those using a uniform quantizer. Experimental results show that the proposed method is the proposed method is superior in PSNR to LuraTech's method by about 1.0 dB, to JPEG by about 5.0 dB for $640\times480$ 24bits color ultrasound image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.510-512
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• 2018

The proposed method enhances the resolution of images by removing noise using wavelet transform to remove noise from images generated by ultrasound diagnosis. We propose an algorithm to reduce the speckle noise and enhance the edge of the ultrasound image. The proposed algorithm can enhance edges of various sizes by using wavelet transform which can use both frequency and spatial information. Experimental results show that the performance of the algorithm for noise reduction of ultrasound images is about 0.45ms for $520{\times}440$ images.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.247-257
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• 2003

This paper proposes a three-dimensional (3D) segmentation algorithm for extracting a diagnostic object from ultrasound images by using a LoG operator In the proposed algorithm, 2D cutting planes are first obtained by the equiangular revolution of a cross sectional Plane on a reference axis for a 3D volume data. In each 2D ultrasound image. a region of interest (ROI) box that is included tightly in a diagnostic object of interest is set. Inside the ROI box, a LoG operator, where the value of $\sigma$ is adaptively selected by the distance between reference points and the variance of the 2D image, extracts edges in the 2D image. In Post processing. regions of the edge image are found out by region filling, small regions in the region filled image are removed. and the contour image of the object is obtained by morphological opening finally. a 3D volume of the diagnostic object is rendered from the set of contour images obtained by post-processing. Experimental results for a tumor and gall bladder volume data show that the proposed method yields on average two times reduction in error rate over Krivanek's method when the results obtained manually are used as a reference data.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.105-116
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• 2021

In order to preserve the seeds of excellent Hanwoo(Korean traditional cattle) and secure quality competitiveness in the infinite competition with foreign imported beef, production of high-quality Hanwoo beef is absolutely necessary. %IMF (Intramuscular Fat Percentage) is one of the most important factors in evaluating the value of high-quality meat, although standards vary according to food culture and industrial conditions by country. Therefore, it is required to develop a %IMF estimation algorithm suitable for Hanwoo. In this study, we proposed a method of estimating %IMF of Hanwoo using CNN in ultrasound images. First, the proposed method classified the chemically measured %IMF into 10 classes using k-means clustering method to apply CNN. Next, ROI images were obtained at regular intervals from each ultrasound image and used for CNN training and estimation. The proposed CNN model is composed of three stages of convolution layer and fully connected layer. As a result of the experiment, it was confirmed that the %IMF of Hanwoo was estimated with an accuracy of 98.2%. The correlation coefficient between the estimated %IMF and the real %IMF by the proposed method is 0.97, which is about 10% better than the 0.88 of the previous method.

• PNF and Movement
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• v.17 no.2
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• pp.329-337
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• 2019

Purpose: Ultrasound imaging is commonly used to measure the pennation angle of human skeletal muscles in vivo. However, manual assessment of the pennation angle using ultrasound images is subjective and time-consuming and requires a high level of examiner skill. The architectural analysis of human skeletal muscles is thus challenging. Automated approaches using image processing techniques are therefore required to estimate the pennation angle in ultrasound images. The purpose of this study was thus to assess the intra-tester and inter-tester reliability and validity of the pennation angle using an automatic measurement program. Methods: Twenty-two healthy young adults (mean age = 22.55 years) with no medical history of neurological or musculoskeletal disorders voluntarily participated in this study. Ultrasound imaging was used to measure the pennation angle of the gastrocnemius muscle at rest. One examiner acquired images from all the participants. The intra-tester and inter-tester reliability were evaluated using the intraclass correlation coefficient (ICC) to estimate reliability. Validity was measured using Pearson's correlation coefficient. Results: The intra-rater reliability was excellent for the automatic pennation angle measuring program and the manual pennation angle assessment method (ICC>0.95). The inter-rater reliability was also excellent for both methods (ICC>0.93). All the correlation coefficients for the automatic pennation angle measuring program and the manual pennation angle assessment method were 0.79, which indicated a significantly positive correlation (p<0.05). Conclusion: Pennation angle measurement using the automatic pennation angle measuring program showed acceptable reliability and validity. This study therefore demonstrated that the automatic measuring program was able to automatically measure the pennation angle of skeletal muscles using ultrasound images, and thus made it easy to investigate skeletal muscle architecture.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.118-127
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• 2024

Commonly deep learning methods for enhancing the quality of medical images use unpaired dataset due to the impracticality of acquiring paired dataset through commercial imaging system. In this paper, we propose a supervised learning method to enhance the quality of ultrasound images. The U-net model is designed by incorporating a divide-and-conquer approach that divides and processes an image into four parts to overcome data shortage and shorten the learning time. The proposed model is trained using paired dataset consisting of 828 pairs of low-quality and high-quality images with a resolution of 512x512 pixels obtained by varying the number of channels for the same subject. Out of a total of 828 pairs of images, 684 pairs are used as the training dataset, while the remaining 144 pairs served as the test dataset. In the test results, the average Mean Squared Error (MSE) was reduced from 87.6884 in the low-quality images to 45.5108 in the restored images. Additionally, the average Peak Signal-to-Noise Ratio (PSNR) was improved from 28.7550 to 31.8063, and the average Structural Similarity Index (SSIM) was increased from 0.4755 to 0.8511, demonstrating significant enhancements in image quality.

• 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.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.169-179
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• 1994

B-mode ultrasound is being used to assess carotid atherosclerosis in epidemiological studies and clinical trials. Recently the interpretation of measurements made from ultrasound images has been questioned. This study examines the anatomical correlates of B-mode ultrasound of carotid arteries in vitro and in situ in cadavers. Twenty-seven segments of human carotid artery were collected at autopsy. pressure perfusion fixed in buffered 2.5% gluteraldehyde and 4% paraformaldehyde and imaged using an ATL UM-8 (10 MHz single crystal mechanical probe). Each artery was then frozen, sectioned and stained with van Gieson or elastin van Gieson. The thickness of the intima. media and adventitia were measured 'to an accuracy of 0.01 mm from histological sections using a calibrated eye graticule on a light microscope. Shrinkage artifact induced by histological preparation was determined to be 7.8%. Digitised ultra sound images of the artery wall were analysed off-line. The distance from the leading edge of the first interface ($LE_{1}$) to the leading edge of the second interface ($LE_2$) was measured using a dedicated programme. $LE_{1}$-$LE_{2}$ measurements were correlated against histological measurements corrected for shrinkage. Mean values for the far wall were: ultra sound $LE_{1}$-$LE_{2}$ (0.97 mm, S.D. 0.26), total wall thickness (1.05 mm, S.D. 0.37), adventitia (0.35 mm, S.D. 0.16), media (0.61 mm, S.D. 0.18). intima (0.09 mm, S.D. 0.13). Ultrasound measurements corresponded best with total wall thickness, rather than elastin or the intima-media complex. Excision of part of the intima plus media or removal of the adventitia resulted in a corresponding decrease in the $LE_{1}$-$LE_{2}$ distance of the B-mode image. Furthermore. increased wall thickness due to intimal atherosclerotic thickening correlated well with $LE_{1}$-$LE_{2}$ distance of the B-mode images. B-mode images obtained from the carotid arteries in situ in four cadavers also corresponded best with total wall thickness measured from histological sections and not with the thickness of the intima plus media. In conclusion, the $LE_{1}$-$LE_{2}$ distance measured on B-mode images of the carotid artery best represents total wall thickness of intima plus media plus adventitia and not intima plus media alone.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.911-917
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• 2023

The method of observing nodular changes on the liver surface using clinical ultrasonography is useful for diagnosing cirrhosis. However, the speckle noise that inevitably occurs in ultrasound images makes it difficult to identify changes in the liver surface and echo patterns, which has a negative impact on the diagnosis of cirrhosis. The purpose of this study is to model the median modified Wiener filter (MMWF), which can efficiently reduce noise in cirrhotic ultrasound images, and confirm its applicability. Ultrasound images were acquired using an ACR phantom and an actual cirrhotic patient, and the proposed MMWF algorithm and conventional noise reduction algorithm were applied to each image. Coefficient of variation (COV) and edge rise distance (ERD) were used as quantitative image quality evaluation factors for the acquired ultrasound images. We confirmed that the MMWF algorithm improved both COV and ERD values compared to the conventional noise reduction algorithm in both ACR phantom and real ultrasound images of cirrhotic patients. In conclusion, the proposed MMWF algorithm is expected to contribute to improving the diagnosis rate of cirrhosis patients by reducing the noise level and improving spatial resolution at the same time.