• Journal of the Korean Society for Precision Engineering
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• v.19 no.12
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• pp.20-26
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• 2002

Deformation phase can be obtained by using Least-Square fitting. In extraction of phase values, Least-Square Fitting is superior to usual method such as 2, 3, 4-Bucket Algorithm. That can extract almost noise-free phase and retain 2 $\pi$ discontinuities. But more fringes in phase map, 2 $\pi$ discontinuities are destroyed when that are filtered and reconstruction of deformation is not reliable. So, we adapted Least-Square fitting using an isotropic window in dense fringe. Using Sine/cosine filter give us perfect 2 $\pi$ discontinuities information. We showed the process and result of extraction of phase map and filtering in this paper.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.209-213
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• 2006

In this paper describes image enhancement technique using deconvolution processing for ultrasonic nondestructive testing. . When flaws are detected for B-scan or C-scan, blurring effect which is caused by the moving intervals of transducer degrades the quality of images. In addition, acquisited images suffer form speckle noise which is caused by the ultrasonic components reflected from the grain boundary of material [1,2]. The deconvolution technique can restore sharp peak value or clean image from blurring signal or image. This processing is applied to C-scan image obtained from known specimen. Experimental results show that the deconvolution processing contributes to get improved the quality of C-scan images.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.476-477
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• 2009

The smallest green laser containing a built-in temperature controlling unit has been demonstrated. The device volume was just 0.5 cubic centimeters, which is nearly the same size as existing red and blue diode lasers, has an electrical-to-optical conversion efficiency of 10% and 150 mW power output. Furthermore as an alternative approach for compact green laser development, a quasi-phase matching method with wide spectral bandwidth for the reduction of speckle noise will be presented.

• Journal of Biomedical Engineering Research
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• v.27 no.1
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• pp.6-13
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• 2006

Ultrasound visualization is a typical diagnosis method to examine organs, soft tissues and fetus data. It is difficult to visualize ultrasound data because the quality of the data might be degraded by artifact and speckle noise, and gathered with non-linear sampling. Rendering speed is too slow since we can not use additional data structures or procedures in rendering stage. In this paper, we use several visualization methods for fast rendering of ultrasound data. First method, denoted as adaptive ray sampling, is to reduce the number of samples by adjusting sampling interval in empty space. Secondly, we use early ray termination scheme with sufficiently wide sampling interval and low threshold value of opacity during color compositing. Lastly, we use bilinear interpolation instead of trilinear interpolation for sampling in transparent region. We conclude that our method reduces the rendering time without loss of image quality in comparison to the conventional methods.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• Progress in Medical Physics
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• v.20 no.2
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• pp.62-71
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• 2009

Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the noninvasive assessment of biological tissues. However, OCT images difficult to analyze due to speckle noise. In this paper, we tested various image processing techniques for speckle removal of human and rabbit cartilage OCT images. Also, we distinguished the images which get with methods of image segmentation for OCT images, and found the most suitable method for segmenting an image. And, we selected image segmentation suitable for OCT before image reconstruction. OCT was a weak point to system design and image processing. It was a limit owing to measure small a distance and depth size. So, good edge matching algorithms are important for image reconstruction. This paper presents such an algorithm, the chamfer matching algorithm. It is made of background for 3D image reconstruction. The purpose of this paper is to describe good image processing techniques for speckle removal, image segmentation, and the 3D reconstruction of cartilage OCT images.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.571-575
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• 1998

One of the most significant features of diagnostic ultrasonic instruments is to provide real time information of the soft tissues movements. Echocardiogram has been widely used for diagnosis of heart diseases since it is able to show real time images of heart valves and walls. However, the currently used ultrasonic images are deteriorated due to presence of speckle noises and image dropout. Therefore, it is very important to develop a new technique which can enhance ultrasonic images. In this study, a technique which extracts enhanced binary images in echocardiograms was proposed. For this purpose, a digital moving image file was made from analog echocardiogram, then it was stored as 8-bit gray-level for each frame. For an efficient image processing, the region containing the heat septum and tricuspid valve was selected as the region of interest(ROI). Image enhancement filters and morphology filters were used to reduce speckle noises in the images. The proposed procedure in this paper resulted in binary images with enhanced contour compared to those form the conventional threshold technique and original image processing technique which can be further implemented for the quantitative analysis of the left ventricular wall motion in echocardiogram by easy detection of the heart wall contours.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.39-42
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• 1994

An automatic multi-threshold algorithm for segmentation of 2D ultrasound images based on average filtering and the characteristics of speckle noise in 2D ultrasound image is proposed. To do this, we investigate the histogram of difference between $7{\times}7$ averaging histogram and $3{\times}3$ averaging histogram. And, we find zero crossing points in the positive portion of the differenced histogram and select middle points of the zero crossing points. We assign these selected points to characteristic points. The thresholds are the center of two characteristic points. Then we segment 2D ultrasound image by using these thresholds and extract edges from applying edge operator to optimal segmented image. Experimental results show that the segmented regions are devided accurately around the homogeneous region.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.7-12
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• 2001

In this paper, an estimation method of hybrid feature was proposed to detect linear feature such as the road network from SAR(synthetics aperture radar) images that include speckle noise. First we considered the mean intensity ratio or the statistical properties of locality neighboring regions to detect linear feature of road. The responses of both methods are combined to detect the entire road network. The purpose of this paper is to extract the segments of road and to mutually connect them according to the identical intensity road from the locally detected fusing images. The algorithm proposed in this paper is to define MRF(markov random field) model of the priori knowledge on the roads and applied it to energy function of interacting density points, and to detect the road networks by optimizing the energy function.