• Korean Journal of Radiology
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• 제21권8호
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• pp.987-997
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• 2020

Objective: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. Materials and Methods: A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. Results: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was -0.17 ± 3.07% for liver volume and -0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. Conclusion: The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.

• Journal of Information Processing Systems
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• 제13권3호
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• pp.491-499
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• 2017

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

• 전자공학회논문지D
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• 제35D권8호
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• pp.76-81
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• 1998

This paper presents a new method to synthesize a digital hologram that meets the resolution of a currently manufactured LCD while capable of displaying a 3-D object. That is accomplished by segmenting a hologram, resulting in relaxed sampling constraint. We show that the segmentation of a hologram enables us to utilize the planewave approximation and, unlike in a holographic stereogram, it does not require projection process, but directly takes fourier transform of horizontally sliced 2-D images that constitute a 3-D object, which makes it possible to reconstruct a higher resolution image with depth information. We also show that proposed hologram contains data significantly smaller than the conventional hologram does, which is quite useful for constructing wide-viewing hologram. Finally, simulation results obtained by both methods are compared.

• Journal of information and communication convergence engineering
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• 제10권4호
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• pp.378-383
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• 2012

This paper presents the implementation of an automatic road sign recognizer for an intelligent transport system. In this system, lists of road signs are processed with actions such as line segmentation, single sign segmentation, and storing an artificial sign in the database. The process of taking the video stream and extracting the road sign and storing in the database is called the road sign recognition. This paper presents a study on recognizing traffic sign patterns using a segmentation technique for the efficiency and the speed of the system. The image is converted from one scale to another scale such as RGB to grayscale or grayscale to binary. The images are pre-processed with several image processing techniques, such as threshold techniques, Gaussian filters, Canny edge detection, and the contour technique.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.138.3-138
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• 2001

Because face has non-rigid structure and is influenced by illumination, we need robust face detection algorithm with the variations of external environments (orientation of lighting and face, complex background, etc.). In this paper we develop a new face detection algorithm to achieve robustness. First we transform RGB color into other color space, in which we can reduce lighting effect much. Second, hierarchical image segmentation technique is used for dividing a image into homogeneous regions. This process uses not only color information, but also spatial information. One of them is used in segmentation by histogram analysis, the other is used in segmentation by grouping. And we can select face region among the homogeneous regions by using facial features.

• ETRI Journal
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• 제39권2호
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• pp.163-172
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• 2017

A high performance license plate recognition system (LPRS) is proposed in this work. The proposed LPRS is composed of the following three main stages: (i) plate region determination, (ii) character segmentation, and (iii) character recognition. During the plate region determination stage, the image is enhanced by image processing algorithms to increase system performance. The rectangular license plate region is obtained using edge-based image processing methods on the binarized image. With the help of skew correction, the plate region is prepared for the character segmentation stage. Characters are separated from each other using vertical projections on the plate region. Segmented characters are prepared for the character recognition stage by a thinning process. At the character recognition stage, a three-layer feedforward artificial neural network using a backpropagation learning algorithm is constructed and the characters are determined.

• 반도체디스플레이기술학회지
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• 제19권3호
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• pp.12-18
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• 2020

In semiconductor manufacturing, defect detection is critical to maintain high yield. Currently, computer vision systems used in semiconductor photo lithography still have adopt to digital image processing algorithm, which often occur inspection faults due to sensitivity to external environment. Thus, we intend to handle this problem by means of using Mask R-CNN instead of digital image processing algorithm. Additionally, Mask R-CNN can be trained with image dataset pre-processed by means of the specific designed digital image filter to extract the enhanced feature map of Convolutional Neural Network (CNN). Our approach converged advantage of digital image processing and instance segmentation with deep learning yields more efficient semiconductor photo lithography inspection system than conventional system.

• Journal of Electrical Engineering and Technology
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• 제7권6호
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• pp.1001-1008
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• 2012

Non-destructive testing (NDT) is a technique used in science and industry to evaluate the properties of a material without causing damage. In this paper we propose a method for segmenting radiographic images of welding in order to extract the welding defects which may occur during the welding process. We study different methods of level set and choose the model adapted to our application. The methods presented here take the property of local segmentation geodesic active contours and have the ability to change the topology automatically. The computation time is considerably reduced after taking into account a new level set function which eliminates the re-initialization procedure. Satisfactory results are obtained after applying this algorithm both on synthetic and real images.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.345-346
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• 2009

The segmentation of vessel including portal vein, hepatic vein and artery, from Computed Tomography (CT) images plays an important role in the therapeutic strategies for hepatic diseases. Representing segmented vessels in three dimensional spaces is extremely useful for doctors to plan liver surgery. In this paper, proposed method is focused on smoothing technique of segmented 3D liver vessels, which derived from 3D region growing approach. A pixel expand algorithm has been developed first to avoid vessel lose and disconnection cased by the next smoothing technique. And then a binary volume filtering technique has been implemented and applied to make the segmented binary vessel volume qualitatively smoother. This strategy uses an iterative relaxation process to extract isosurfaces from binary volumes while retaining anatomical structure and important features in the volume. Hard and irregular place in volume image has been eliminated as shown in the result part, which also demonstrated that proposed method is a suitable smoothing solution for post processing of fine vessel segmentation.

• 대한전자공학회논문지SP
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• 제42권2호
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• pp.1-8
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• 2005

지금까지 많은 영상 영역분할 방법들이 연구되고 있으나 이들 방법들은 잡음영상에 대해서는 주로 백색잡음과 같은 일반적인 환경에서 영상을 처리하였지만, 충격성 잡음 영상에 대해서는 영상과 잡음을 정확히 구별할 수 있는 파리미터를 추출하기가 어렵기 때문에 효과가 떨어지는 문제점을 가지고 있다. 그래서 기존 방법을 이용한 모든 응용분야에는 충격성 잡음에 따른 성능 저하의 잠재성이 항상 내포되어 있다. 본 논문에서는 충격성 잡음 영상에 효과적으로 영상정보 파라미터를 추출할 수 있는 사분위편차(quartile deviation) 기반 쿼드트리 영역분할 방법을 제안한다. 본 방식은 영상데이터의 전송이나 처리과정에서 포함될 수 있는 충격성 잡음을 영상정보로부터 판별할 수 있는 장점을 가기지 때문에 다양한 영상처리 분야에 응용할 수 있다. 실험적인 비교를 통해서 제안한 쿼드트리 영역분할 방법이 충격성 잡음이 첨가된 환경에서 영상정보 파라미터를 정확히 추정할 수 있다는 것을 확인할 수 있음을 검증하였다.