• Progress in Medical Physics
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• v.15 no.2
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• pp.112-119
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• 2004

We tried to build human models based on medical images of live Korean, instead of using standard data of human body structures. Characteristics of mechanical structures of human bodies were obtained from medical images such as CT and MR images. For each constitutional part of mechanical structures CT images were analyzed in terms of gray levels and MR images were analyzed in terms of pulse sequence. Characteristic features of various mechanical structures were extracted from the analyses. Based on the characteristics of each structuring element we peformed image segmentation on CT and MR images. We delineated bones, muscles, ligaments and tendons from CT and MR images using image segmentation or manual drawing. For the image segmentation we compared the edge detection method, region growing method and intensity threshold method and applied an optimal compound of these methods for the best segmentation results. Segmented mechanical structures of the head/neck part were three dimensionally reconstructed.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.71-78
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• 2010

Even though various types of image segmentation methods have been extensively introduced, robustly segmenting images to environmental conditions such as illumination changes, shading, highlight, etc, has been known to be a very difficult task. To resolve the problem in some degree, we propose in this paper an environment-adaptive image segmentation approach using color invariants. The suggested method first introduces several color invariants like W, C, U, N, and H, and automatically measures environmental conditions in which images are captured. It then chooses the most adequate color invariant to environmental factors, and effectively extracts edges using the selected invariant. Experimental results show that the proposed method can robustly perform edge-based segmentation rather than existing methods. We expect that our method will be useful in many real applications which require edge-based image segmentation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.25-33
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• 2010

We propose a retouching method that converts a general photography to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform DoG(Difference of Gradient) edge extraction and mean shift segmentation respectively from the bilateral filtered image. The DoG edge extraction is performed using luminance component of the image whose RGB color space is transformed into CIELAB space. Experimental result shows that our method can be applied to various types of image and bring better result, especially against the photo taken in daylight.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.355-366
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• 2002

The web is one of the most plentiful sources of images. The web has an immediate need for image processing technology in Java. This paper provides a practical introduction to statistical image processing using Java on the web. The paper describes how images are represented in Java and deals with four image processing operations based on basic statistical methods: point processing, spatial filtering, edge detection and image segmentation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.3
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• pp.105-112
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• 1989

A new simple edge-based segmentation method composed of edge tracing, region filling, and post processing is proposed. Solving so called the small gap problem common to most of edge-based methods, this method segments images so completely as to be suitable for image coding. Experimental results show that our methods has much less (1/3) computation time than Perkins' one, and its reconstructed images is good on visual perception.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.615-622
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• 2018

Recent advancements in camera communication (CamCom) technology using visible light exploited to use display as an luminance source to modulate the data for visible light data communication. The existing display-CamCom techniques uses the selected region of interest based camera capturing approach to detect and decode the 2D color coded data on display screen. This is not effective way to do communicate when the user on mobility. This paper propose the automatic display detection using Lambertian color segmentation combined with canny edge detection algorithms for CamCom in order to avoid manual region of interest selection to establish communication link between display and camera. The automatic display detection methods fails using conventional edge detection algorithms when content changes dynamically in displays. In order to solve this problem lambertian color segmentation combined with canny edge detection algorithms are proposed to detect display automatically. This research analysed different algorithms on display edge recognition and measured the performance on rendering dynamically changing content with color code on display. The display detection rate is achieved around 96% using this proposed solutions.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.95-98
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• 2001

Edge detection is an important problem in computer vision and image understanding. Because the threshold is difficult to properly determine, edge images gained by the usually gradient-based segmentation methods are often tend to have many disjoint or overlapping boundaries, which makes the edge images spinous. In this paper, a practical multilevel morphological filter is presented for smoothing spinous edge images. The experimental results show that the method is effective in dealing with the images of a target in the sky.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.59-62
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• 2002

Watershed Segmentation with Multiple Merging Conditions in Region Growing Process The watershed segmentation method holds the merits of edge-based and region-based methods together, but still shows some problems such as over segmentation and merging fault. We propose an algorithm which overcomes the problems of the watershed method and shows efficient performance for .general images, not for specific ones. The algorithm segments or merges regions by thresholding the depths of the catchment basins, the similarities and the sizes of the regions. The experimental results shows the reduction of the number of the segmented regions that are suitable to human visual system and consciousness.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1448-1455
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• 1990

In this paper, a range image segmentation method is proposed. This method consists of an initial segmentation stage by discontinuous edge detection and surface type labeling based on the sign of the principal curvatures. Initially type labeled image is oversegmented, this image is merged via stepwise optimal region merging stage based on polynomial function approxiamtion. The successful segmentation results are presented for two synthetic range images with noise and a real-world ERIM range image.