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International Conference on Automation, Communication, Architectonics and Materials

作者： Wang, Xian-qiu Wang, Xiu-bi Huang, Xiao-li Sichuan Univ Sci & Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9783037851036

Image segmentation is one of the fundamental problems in image processing and computer vision. Studies of high quality image segmentation methods have always gained a lot of attention in the field of image processing. However, so far the problem of image segmentation has not been well solved yet. Conventional methods cannot divide the images exactly because too much objectivity has been used. For complex objects, this paper proposed an efficient image segmentation algorithm based wavelet transform. This article presents the result of wavelet image segmentation and watershed algorithm image segmentation. The experimental result indicates that, the algorithm based on wavelet transform has fast convergence and good noise immunity.

关键词： Image Segmentation Wavelet Transform watershed algorithm

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Real-time image segmentation based on a parallel and pipelined watershed algorithm

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2007年第4期2卷 319-329页

作者： Dang Ba Khac Trieu Maruyama, Tsutomu Univ Tsukuba Tsukuba Ibaraki 3058573 Japan

The watershed transformation is a popular image segmentation technique for gray scale images. This paper describes a real-time image segmentation based on a parallel and pipelined watershed algorithm which is designed for hardware implementation. In our algorithm: (1) pixels in a given image are repeatedly scanned from top-left to bottom-right, and then from bottom-right to top-left, in order to achieve high performance on a pipelined circuit by simplifying memory access sequences, (2) all steps in the algorithm are executed at the same time in the pipelined circuit, (3) the amount of data that are scanned is gradually reduced as the calculation progresses by memorizing which data are modified in the previous scan, and (4) N pixels can be processed in parallel. In our current implementation on an off-the-shelf field-programmable gate array board, up to four pixels can be processed in parallel. The performance for 512 x 512 pixel images is fast enough to be the first step in real-time applications.

关键词： watershed algorithm Segmentation Real time FPGA

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Study on Coordinate Information Generation Method of Interested Area in IMRT Inverse Planning System

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International Conference on Materials Engineering for Advanced Technologies (ICMEAT2011)

作者： Chen, Zhen Li, Guoli Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Technol Sch Informat Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783037851517

In inverse planning system of Intensity Modulated Radiation Therapy (IMRT), the coordinate of interested area (target, organ at risk) is the necessary information for optimization algorithm. This paper analyzes several classical region filling algorithms firstly. Based on the VTK toolkits, Bezier curve algorithm, and the improved watershed algorithm, a method using interactive delineation tool to save the coordinate information of interested area is realized. The information in the delineated area is accurately extracted by the method.

关键词： IMRT inverse planning interested area Visualization Toolkit Bezier Curse algorithm watershed algorithm

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Automatic Location of the Talairach Cortical Landmarks from T2-weighted MR Images

Automatic Location of the Talairach Cortical Landmarks from ...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Fu, Yili Zhang, GuangCai Chang, Qiuyue Wang, Shuguo Han, Xianwei Harbin Inst Technol State Key Lab Robot & Syst Harbin 150006 Peoples R China

ISBN: (纸本)9783037850176

For labeling the T2-weighted MR images using human brain atlas, it is prerequisite to the foundation of the Talairach space for T2W MR images, and the basic condition to found Talairach space is the location of Talairach cortical landmarks from T2W MR images. A method to locate the Talairach cortical landmarks from T2W MR images is proposed, it consists of three aspects: Firstly, determine the planes including the six cortical landmarks;segment the planes based on fuzzy C-means clustering algorithm, gray level projection, watershed algorithm, region merging, thresholding, and morphologic operations;locate the cortical landmarks from the segmented planes. The algorithm has been validated quantitatively with 20 T2W MR images data sets. The mean errors of the Talairach cortical landmarks were below 1.00 mm. It took about 8 seconds for identifying them on P4 3.0 GHz. This fast, robust algorithm is potentially useful in clinic and for research.

关键词： Talairach cortical landmarks T2-weighted MR image FCM algorithm watershed algorithm region merging thresholding

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Ceramic image processing using the second curvelet transform and watershed algorithm

Ceramic image processing using the second curvelet transform...

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IEEE International Conference on Robotics and Biomimetics (ROBIO 2007)

作者： Li, Qingwu Ni, Xue Liu, Guogao Hohai Univ Coll Comp & Informat Engn Changzhou 213022 Peoples R China

ISBN: (纸本)9781424417612

Ceramic materials physical properties are the results from their microstructures characteristics. So, it is very important to quantify these characteristics in an accurate way. Ceramic microscopic image processing may be divided into two main procedures: image pre-processing for noise :reduction and edges enhancement to clarify the image and image segmentation for locating and detecting the objects of interest. Conventional 2D wavelet transform is separable and thus can not sparsely represent non-separable structures of the image, such as directional curves. Curvelet transform is a new extension to wavelet transform in two dimensions. The directionality feature of curvelet transform makes it a good choice for representation of curves and edges in the image. The second generation curvelet transform theory makes it understood and implemented more easily. In this paper, a new enhancement function is proposed to enhance the edges by curvelet transform. The method is also compared with two usual contrast enhancement methods, histogram equalization and wavelet based contrast enhancement. It is shown that the contrast enhancement method based on curvelet transform is superior to both histogram equalization and the method based on wavelet transform, in both enhancement of fine structures and reduction of noise in the images. This enhancement method is used to ceramic images lore-processing, and then watershed algorithm is applied to the segmentation. The grain size distributions can be obtained from segmentation images. It has been proved that this method is effective for the ceramic grain image.

关键词： ceramic image second curvelet transform contrast enhancement watershed algorithm

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Quantum-Inspired Immune Clonal Clustering algorithm Based on watershed

Quantum-Inspired Immune Clonal Clustering Algorithm Based on...

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2010 IEEE World Congress on Computational Intelligence

作者： Li, Yangyang Wu, Nana Ma, Jingjing Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9781424481262

Based on the concepts and principles of quantum computing, a novel clustering algorithm, called a quantum-inspired immune clonal clustering algorithm based on watershed (QICW), is proposed to deal with the problem of image segmentation. In QICW, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody's updating, the quantum mutation operator is applied to accelerate convergence. The quantum recombination realizes the information communication between the subpopulation groups so as to avoid premature convergences. In this paper, the segmentation problem is viewed as a combinatorial optimization problem, the original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with the genetic clustering algorithm based on watershed (W-GAC), and the k-means algorithm based on watershed (W-KM).

关键词： SAR image segmentation combinatorial mathematics combinatorial optimization problem genetic algorithms genetic clustering algorithm image segmentation image texture k-means algorithm maximum affinity function multistate gene quantum bits pattern clustering quantum computing quantum computing quantum mutation operator quantum-inspired immune clonal clustering algorithm synthetic aperture radar texture image watershed algorithm

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Color Image Segmentation Based on watershed and Ncut of Improved Weight Matrix

Color Image Segmentation Based on Watershed and Ncut of Impr...

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2011 IEEE International Conference Mechatronics and Automation(2011 IEEE机电一体化与自动化国际会议)

作者： Xiaoli Xu Zhimao Lu Haiyan Li Information and Communication Engineering College Harbin Engineering University Department of Electr Information and Communication Engineering College Harbin Engineering University

A new color image segmentation method combining twice used watershed and Ncut of improved the weight matrix algorithm is presented in this paper. It preprocesses an image by the twice used watershed algorithm to form segmented regions that preserve the desirable discontinuity characteristics of image. The segmented regions, instead of the image pixels are then represented by using the graph structure and considered as the input image of Ncut algorithm. In addition a new weight matrix is designed in this paper according to the image color and space information. Then the Ncut method is applied to perform globally optimized clustering. Because the image clustering uses the segmented regions, instead of the image pixels, the new method can effectively reduce the computational complexity of traditional Ncut method by using secondary watershed algorithm. The new weight matrix also has certain selfadaptability. Through a large number of experiments using color natural scene images, the results show that the proposed method has superior performance and less computational costs compared to the traditional Ncut algorithm and the method combining the mean shift (MS) and Ncut.

关键词： watershed algorithm Ncut Weight matrix Color image segmentation Mean shift (MS)

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Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography

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JOURNAL OF CHROMATOGRAPHY A 2011年第38期1218卷 6792-6798页

作者： Latha, Indu Reichenbach, Stephen E. Tao, Qingping Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA GC Image LLC Lincoln NE 68505 USA

Comprehensive two-dimensional gas chromatography (GCxGC) is a powerful technology for separating complex samples. The typical goal of GCxGC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GCxGC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Two-dimensional chromatography Comprehensive two-dimensional gas chromatography (GCxGC) Chemometrics Peak detection watershed algorithm Two-step peak detection

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A pipeline for interactive cortex segmentation

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COMPUTER SCIENCE-RESEARCH AND DEVELOPMENT 2011年第1-2期26卷 87-96页

作者： Wellein, Daniela Born, Silvia Pfeifle, Matthias Duffner, Frank Bartz, Dirk Univ Leipzig VCM ICCAS Semmelweisstr 14 D-04103 Leipzig Germany Univ Klinikum Tubingen Neurochirurg Klin D-72076 Tubingen Germany

In various clinical or research scenarios, such as neurosurgical intervention planning, diagnostics, or clinical studies concerning neurological diseases, cortex segmentation can be of great value. As, e.g., the visualization of the cortical surface along with target and risk structures enables conservative access planning and gives context information about the patient-specific anatomy. We present an interactive cortex segmentation pipeline (CSP) for T1-weighted MR images, utilizing watershed and level set methods. It is designed to allow the user to adjust the intermediate results at any stage of the segmentation process. Particular attention is paid to the appropriate visualization of the segmentation in the context of the original data for verification and to different interaction methods (manual editing, parameter tuning, morphological operations). Evaluation of the interactive CSP is performed with the Segmentation Validation Engine (SVE) by Shattuck et al. (NeuroImage 45(2): 431-439, 2009). The segmentation quality of our method is comparable to the best results of three different established methods: the brain extraction tool (BET), brain surface extractor (BSE), and hybrid watershed algorithm (HWA). Being designed for interaction, the CSP integrates the users' expertise by allowing him to perform correction at any stage of the pipeline, enabling him to easily achieve a segmentation fulfilling his specific needs.

关键词： Cortex segmentation watershed algorithm Level set method Segmentation evaluation Neurosurgical intervention planning User interaction

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An algorithm for Segmenting Spots in Crop Leaf Disease Image with Complicated Background

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SENSOR LETTERS 2010年第1期8卷 61-65页

作者： Diao, Zhihua Guo, Xinyu Zhao, Chunjiang Lu, Shenglian Wen, Weiliang Natl Engn Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Anhui Peoples R China

A segmentation algorithm is proposed in this paper to deal with the problem of segmenting spots from crop leaf disease image with complicated background. Firstly the profile of the initial image is extracted by using a watershed algorithm from the gray-scale image, and the binary image is obtained by combining a threshold processing and morphological operations. Then the accurate leaf disease image is extracted by using window clipping and image restoration. Finally, the spots are segmented from the leaf disease image by using green segmentation algorithm, and the accurate spot information can be obtained after noise reduction. The experimental result shows that the proposed algorithm can be used in complicated field environment with high extracting accuracy.

关键词： Image Segmenting watershed algorithm Morphological Processing Noise Elimination Complicated Background

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