• Journal of Biomedical Engineering Research
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• v.21 no.3 s.61
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• pp.239-246
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• 2000

In this paper. we propose a semi-automatic segmentation algorithm to extract organ in 3D medical data by using a manually segmentation result in a sing1e slice. Generally region glowing based tracking method consists of 3 steps object projection. seed extraction and boundary decision by region growing. But because the boundary between organs in medical data is vague, improper seeds make the boundary dig into the organ or extend to the false region. In the proposed algorithm seeds are carefully extracted to find suitable boundaries between organs after region growing. And the jagged boundary at low gradient region after region growing is corrected by post-processing using Fourier descriptor. Also two-path tracking make it possible to catch up newly appeared areas. The proposed algorithm provides satisfactory results in segmenting 1 mm distance kidneys from X-rav CT body image set of 82 slices.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.3-5
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• 2005

Vessel boundary detection and modeling is a difficult but a necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. In this paper we present a method of analyzing the structure by means of an active contour model(using GVF Snake) for vessel boundary detection and 3D reconstruction. For this purpose we used a virtual vessel model and produced a phantom model. From these phantom images we obtained the contours of the vessel by GVF Snake and then reconstructed a 3D structure by using the coordinates of snakes.

• Journal of Mechanical Science and Technology
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• v.14 no.10
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• pp.1104-1113
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• 2000

This paper proposes an efficient reverse engineering technique for compound surfaces using a boundary detection method. This approach consists in extracting geometric edge information using a vision system, which can be used in order to drastically reduce geometric errors in the vicinity of compound surface boundaries. Through the image-processing technique and the interpolation process, boundaries are reconstructed by either analytic curves (e. g. circle, ellipse, line) or parametric curves (B-spline curve). In other regions, except boundaries, geometric data are acquired on CMM as points inspected using a touch type probe, and then they are interpolated on several surfaces using a B-spline skinning method. Finally, the boundary edge and the skinned surfaces are combined to reconstruct the final compound surface. Through simulations and experimental works, the effectiveness of the proposed method is confirmed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.49-58
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• 2012

Buildings become complex and diverse with time. It is difficult to extract individual buildings using only an optical image, because they have similar spectral characteristics to objects such as vegetation and roads. In this study, we propose a method to extract building area and boundary through integrating airborne Light Detection and Ranging(LiDAR) data and aerial images. Firstly, a binary edge map was generated using Edison edge detector after applying Adaptive dynamic range linear stretching radiometric enhancement algorithm to the aerial image. Secondly, building objects on airborne LiDAR data were extracted from normalized Digital Surface Model and aerial image. Then, a temporary building areas were extracted by overlaying the binary edge map and building objects extracted from LiDAR data. Finally, some building boundaries were additionally refined considering positional accuracy between LiDAR data and aerial image. The proposed method was applied to two experimental sites for validation. Through error matrix, F-measure, Jaccard coefficient, Yule coefficient, and Overall accuracy were calculated, and the values had a higher accuracy than 0.85.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.101-109
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• 2012

In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation inTRUS images using support vectors and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. Gabor filter bank for extracting the texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. The boundary of prostate is extracted by the snake-like contour algorithm. The results showed that this new algorithm extracted the prostate boundary with less than 9.3% relative to boundary provided manually by experts.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.139-152
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• 2004

This paper proposes to improve the results of image classification with spatial region growing segmentation by using an MRF-based classifier. The proposed approach is to re-classify the pixels in the boundary area, which have high probability of having classification error. The MRF-based classifier performs iteratively classification using the class parameters estimated from the region growing segmentation scheme. The proposed method has been evaluated using simulated data, and the experiment shows that it improve the classification results. But, conventional MRF-based techniques may yield incorrect results of classification for remotely-sensed images acquired over the ground area where has complicated types of land-use. A multistage MRF-based iterative class-modification in boundary is proposed to alleviate difficulty in classifying intricate land-cover. It has applied to remotely-sensed images collected on the Korean peninsula. The results show that the multistage scheme can produce a spatially smooth class-map with a more distinctive configuration of the classes and also preserve detailed features in the map.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.769-778
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• 2004

Object segmentation is a challenging Problem when the background is cluttered and the objects are overlapped one another. Recent develop-ment using snake algorithms proposed to segment objects from a 2-D Image presents a higher possibilityfor getting better contours. However, the performance of those snake algorithms degrades rapidly when the background is cluttered and objects are overlapped one another, Moreover, the initial snake point placement is another difficulty to be resolved. Here, we propose a novel snake algorithm for object segmentation using disparity information taken from a set of stereo images. By applying our newly designed snake energy function defined in the disparity space, our algorithmeffectively circumvents the limitations found in the previous methods. The performance of the proposed algorithm has been verified by computer simulation using various stereo image sets. The experiment results have exhibited a better performance over the well-known snake algorithm in terms of segmentation accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.1023-1032
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• 2003

Object-based coding, such as MPEG-4, enables various content-based functionalities for multimedia applications. In order to support such functionalities, as well as to improve coding efficiency, each frame of video sequences should be segmented into video objects. In this paper. we propose an effective video object segmentation method using nonlinear multiscale filtering and spatio-temporal information. Proposed method performs a spatial segmentation using a nonlinear multiscale filtering based on the stabilized inverse diffusion equation(SIDE). And, the segmented regions are merged using region adjacency graph(RAG). In this paper, we use a statistical significance test and a time-variant memory as temporal segmentation methods. By combining of extracted spatial and temporal segmentations, we can segment the video objects effectively. Proposed method is more robust to noise than the existing watershed algorithm. Experimental result shows that the proposed method improves a boundary accuracy ratio by 43% on "Akiyo" and by 29% on "Claire" than A. Neri's Method does.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.305-320
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• 2004

Moving object segmentation is a fundamental function for content-based application. Moving object edges are produced by matching the detected moving edges with the current frame edges. But we can often experience the object edge disconnectedness due to coincidence of similarity between the object and background colors or the decrease of movement of moving object. The edge disconnectedness is a serious problem because it degrades the object visual quality so conspicuously That it sometimes makes it inadequate to perform content-based coding. We have solved this problem by developing a robust and comprehensive edge linking algorithm. And we also developed an automatic moving object segmentation algorithm. These algorithms can produce the completely linked moving object edge boundary and the accurate moving object segmentation. These algorithms can process CIF 30 frames/sec in a PC. These algorithms can be used for the MPEG-4 content-based coding.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.43-52
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• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.