• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.865-871
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• 2022

In this paper, we propose a method of extracting prostate region using morphological characteristics of ultra-sonic image of prostate. In the first step of the proposed method, the edge area of the prostate image is extracted. The histogram of ultra-sonic image is used to extract base objects to detect the upper edge of prostate region by altering the contrast of the image, then, the lower edges of the extracted base objects are connected by using monotone cubic spline interpolation to extract the upper edge. Step 2, Otsu's binarization is applied to the region under the extracted upper edge of the prostate ultra-sonic image to extract the lower edge of prostate. In the last step, the upper and the lower edges are connected to extract prostate region and by comparing the extracted region of prostate with the one measured manually, the result showed that the morphological characteristics of prostate in ultrasonic image can be utilized to extract the prostate region.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.695-699
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• 2009

In this paper, we propose an automatic prostate segmentation technique using image intensity and gradient information. Our method is composed of four steps. First, rays at regular intervals are generated. To minimize the effect of noise, the start and end positions of the ray are calculated. Second, the profiles on each ray are sorted based on the gradient. And priorities are applied to the sorted gradient in the profile. Third, boundary points are extracted by using gradient priority and intensity distribution. Finally, to reduce the error, the extracted boundary points are corrected by using B-spline interpolation. For accuracy evaluation, the average distance differences and overlapping region ratio between results of manual and automatic segmentations are calculated. As the experimental results, the average distance difference error and standard deviation were 1.09mm $\pm0.20mm$. And the overlapping region ratio was 92%.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.163-169
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• 2016

In Korea, prostate cancer accounted for generating growth rate second the following thyroid cancer, because of western dietary habits. Survival rate of prostate cancer after clinical behavior is changed depend on follow-up management. A telemedicine have been applied to replacement of medical specialist in rural area, and a quick reaction to emergency situation. Our study developed prostate 3-dimensional (3D) visualization program and designed prostate aftercare system architecture, called smart care, using a device that can access the Internet. Region of interest (ROI) in prostate was manually segmented by physicians and visualized to 3D objects and sent to PACS Server as DICOM images. So, medical personnel could confirm patients' data along with 3D images not only PACS system, but also portable device like a smart phone. As a result, we conducted the aftercare service to 98 patients and visualize 3D prostate images. 3D images had advantage to instinctively apprehend where lesion is and make patients to understand state of their disease easily. In the future, should conduct an aftercare service to more patients, and will obtain numerical index through follow-up study to an accurate analysis.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.279-280
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• 2008

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.145-146
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• 2017

본 논문에서는 남성의 하반신을 촬영한 MRI 영상으로부터 전립선을 분할하는 알고리즘을 제안한다. 우선 3 차원 입체 영상을 학습하기 위해 3D 컨볼루션 계층(convolutional layer) 및 3D 풀링 계층(pooling layer)에 기반한 네트워크를 제안한다. 다음으로 네트워크의 최후단에 해당하는 전연결 계층(fully connected layer)의 강인한 학습을 돕는 잡음 계층을 제안한다. 잡음 계층은 네트워크의 학습 파라미터 혹은 출력 영상에 가우시안 잡음를 더함으로써 드롭 아웃과 같이 훈련 영상에 대한 과적합(overfitting)을 막고 테스트 영상에 강인한 네트워크의 학습을 돕는다. 마지막으로 실험을 통해 제안하는 기법이 기존 기법에 비해 우수한 분할 성능을 보임을 확인한다.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.