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An efficient and robust algorithm for 3D mesh segmentation

MULTIMEDIA TOOLS AND APPLICATIONS 2006年第2期29卷 109-125页

作者： Chen, Lijun Georganas, Nicolas D. Univ Ottawa Sch Informat Technol & Engn Ottawa ON K1N 6N5 Canada

This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.

关键词： 3D mesh Gaussian curvature concaveness XMR neighborhood watershed algorithm region merging

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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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Improved segmentation of low-contrast lesions using sigmoid edge model

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2016年第7期11卷 1267-1283页

作者： Foruzan, Amir Hossein Chen, Yen-Wei Shahed Univ Fac Engn Dept Biomed Engn Tehran Iran Ritsumeikan Univ Coll Informat Sci & Engn Intelligent Image Proc Lab Kusatsu Shiga Japan

The intensity profile of an image in the vicinity of a tissue's boundary is modeled by a step/ramp function. However, this assumption does not hold in cases of low-contrast images, heterogeneous tissue textures, and where partial volume effect exists. We propose a hybrid algorithm for segmentation of CT/MR tumors in low-contrast, noisy images having heterogeneous/homogeneous or hyper-/hypo-intense abnormalities. We also model a smoothed noisy intensity profile by a sigmoid function and employ it to find the true location of boundary more accurately. A novel combination of the SVM, watershed, and scattered data approximation algorithms is employed to initially segment a tumor. Small and large abnormalities are treated distinctly. Next, the proposed sigmoid edge model is fitted to the normal profile of the border. The estimated parameters of the model are then utilized to find true boundary of a tissue. We extensively evaluated our method using synthetic images (contaminated with varying levels of noise) and clinical CT/MR data. Clinical images included 57 CT/MR volumes consisting of small/large tumors, very low-/high-contrast images, liver/brain tumors, and hyper-/hypo-intense abnormalities. We achieved a Dice measure of and average symmetric surface distance of mm. Regarding IBSR dataset, we fulfilled Jaccard index of . The average run-time of our code was s. Individual treatment of small and large tumors and boundary correction using the proposed sigmoid edge model can be used to develop a robust tumor segmentation algorithm which deals with any types of tumors.

关键词： Sigmoid edge model Scattered data approximation Tumor segmentation watershed algorithm Medical image processing

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A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D Confocal microscope images

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CYTOMETRY PART A 2007年第9期71A卷 724-736页

作者： Lin, Gang Chawla, Monica K. Olson, Kathy Barnes, Carol A. Guzowski, John F. Bjornsson, Christopher Shain, William Roysam, Badrinath Rensselaer Polytech Inst ECSE Dept Troy NY 12180 USA Rensselaer Polytech Inst Ctr Subsurface Sensing & Imaging Syst Troy NY 12180 USA Univ Arizona Inst Brain Tucson AZ USA Univ Calif Irvine Ctr Neurobiol Learning & Memory Irvine CA USA Wadsworth Ctr NYS DOH Cent Nervous Syst Disorders Albany NY USA

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations. (c) 2007 International society for Analytical Cytology.

关键词： cell nuclei segmentation classification watershed algorithm region merging model-based Bayesian estimator parzen window batch processing 3D confocal microscopy

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TARGET DETECTION AND ANALYSIS OF INTELLIGENT AGRICULTURAL VEHICLE MOVEMENT OBSTACLE BASED ON PANORAMIC VISION

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INMATEH-AGRICULTURAL ENGINEERING 2019年第3期59卷 277-284页

作者： Wu Weibing Tongling Univ Sch Elect Engn Tongling 244061 Anhui Peoples R China

Agricultural automation and intelligence have a wide range of connotations, involving navigation, image, model, strategy and other engineering disciplines. With the development of modern agriculture, intelligent agricultural vehicles are applied in many engineering areas. The operating environment of agricultural vehicles is very complex, especially as they often face obstacles, affecting the intelligent operation of agricultural vehicles. The traditional obstacle detection mostly uses the limited detection algorithm, in the case of which it is difficult to achieve the moving target detection of panoramic vision. In this paper, mean shift algorithm is selected to detect the moving obstacles of intelligent agricultural vehicles, and adaptive colour fusion is introduced to optimize the algorithm to solve the problems of mean shift. In order to verify the effect of the improvement and application of the algorithm, the video image obtained by the intelligent agricultural vehicle is selected for the simulation experiment, and the best combination (- 0.8.0.2) is obtained for the unequal spacing sampling method. In the process of colour selection, the coefficient needs to be adjusted continuously to improve the tracking accuracy of the algorithm. Further it can be seen that when using a variety of different quantitative methods for comparative analysis, the quantitative method of HIS-360 level is determined.

关键词： panoramic vision intelligent agricultural vehicle motion disorder watershed algorithm

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Aggregate boundary recognition of asphalt mixture CT images based on convolutional neural networks

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ROAD MATERIALS AND PAVEMENT DESIGN 2024年第5期25卷 1127-1143页

作者： Peng, Yong Yang, Han-duo Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China

This study aims to propose an intelligent aggregate boundary segmentation algorithm based on convolutional neural networks (CNNs) and watershed algorithm for quickly recognising the boundary of aggregates on asphalt mixture CT images. CNN was concisely introduced. An aggregate boundary segmentation method for asphalt mixture CT images based on CNN and watershed algorithm was depicted in detail. The generalisation ability, that is, the effectiveness of image segmentation method by CNN and watershed algorithm was also evaluated. Results showed that the intelligent segmentation algorithm proposed by combining CNNs and watershed algorithm could effectively segment the aggregate boundaries on asphalt mixture CT images with different levels of boundary definition. The adhesion between aggregates on asphalt mixture CT images could be reduced using a custom multi-threshold segmentation (CMTS) method. The intelligent image segmentation algorithm had more accurate segmentation and more convenient operation than Canny and multi-threshold algorithms.

关键词： Asphalt mixture CT image aggregate boundary segmentation convolutional neural network watershed algorithm

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An automatic multi-thread image segmentation embedded system for surface plasmon resonance sensor

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SENSORS AND ACTUATORS A-PHYSICAL 2019年 285卷 603-612页

作者： Wang, Chao Ko, Mong-Chi Chen, Yi-Ming Chen, Le-Qun Lin, Chii-Wann Natl Taiwan Univ Inst Biomed Elect & Bioinformat Taipei 10617 Taiwan Natl Taiwan Univ Inst Biomed Engn Taipei 10617 Taiwan Natl Taiwan Univ Inst Appl Mech Taipei 10617 Taiwan

In order to reduce the uncertainties associated with manual selection of regions of interest (ROIs) commonly used in Surface Plasmon Resonance (SPR) imaging system, we proposed and implemented an automatic image segmentation method in an embedded system to facilitate the potential real-time applications. Intuitive marker-controlled watershed algorithm is developed to segment ROIs (reaction, blank, and background regions) from images acquired from an experimental image SPR system. The marker assignment algorithms and pre-processing algorithms are executed in parallel by multi-threading programming on the multi-core embedded system to both real-time and good quality of segmentation. This method exhibited a good robustness in a series of ROIs segmentation test. Furthermore, the intensity response from triplicate detection of glucose standard solutions indicated a good reproducibility of data. The linear range was from 2.5 mg/mL to 20.4 mg/mL, with a correlation coefficient (R-2) of 0.999 and sensitivity of 2.69 a.u./mg/mL. In conclusion, the proposed automatic image segmentation method effectively makes the measurement more precise and simplified. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Surface plasmon resonance (SPR) Optical sensor Biosensor Image segmentation watershed algorithm

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Quality Evaluation of DIBR-Synthesized Images Based on Holes and Expanded Regions

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IEEE ACCESS 2020年 8卷 10640-10648页

作者： Zhao, Yue Wang, Laihua Qi, Sumin Wang, Weisheng Jia, Qing Qufu Normal Univ Sch Software Jining 273165 Shandong Peoples R China

With the Depth-image-based rendering (DIBR) technique, virtual views can be drawn at any position between two reference viewpoints. However, DIBR algorithm may introduce various distortions in the synthesized images, such as holes, artifact, and stretching. This paper proposes a quality evaluation method for DIBR-synthesized images by analyzing the holes and expanded regions. First, the holes regions are obtained with the watershed algorithm and used to evaluate the quality of the DIBR-synthesized images. Then, the synthesized image is compared with the reference image in the sub-channel, and the number of different pixel values is used to assess the global quality of synthesized images. Finally, the two measures are pooled to calculate the overall quality score of the DIBR synthesized image. Experimental results show that our scheme can better predict the image quality and performs better in terms of implementation time compared with other schemes.

关键词： DIBR synthesized image quality evaluation watershed algorithm expanded regions

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watershedCounting3D: A new method for segmenting and counting punctate structures from confocal image data

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TRAFFIC 2007年第4期8卷 339-346页

作者： Gniadek, Thomas J. Warren, Graham Yale Univ Sch Med Dept Cell Biol New Haven CT 06520 USA

Current research in cell biology frequently uses light microscopy to study intracellular organelles. To segment and count organelles, most investigators have used a global thresholding method, which relies on homogeneous background intensity values within a cell. Because this is not always the case, we developed watershedCounting3D, a program that uses a modified watershed algorithm to more accurately identify intracellular structures from confocal image data, even in the presence of an inhomogeneous background. We give examples of segmenting and counting endoplasmic reticulum exit sites and the Golgi apparatus.

关键词： confocal ER exit site Golgi segmentation watershed algorithm

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Standardized assessment of bone micromorphometry around teeth following orthodontic tooth movement A μCT split-mouth study in mice

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JOURNAL OF OROFACIAL ORTHOPEDICS-FORTSCHRITTE DER KIEFERORTHOPADIE 2022年第6期83卷 403-411页

作者： Trelenberg-Stoll, Viktoria Wolf, Michael Busch, Caroline Drescher, Dieter Becker, Kathrin Univ Klinikum Dusseldorf Dept Oral Surg Dusseldorf Germany Univ Klinikum RWTH Aachen Dept Orthodont Aachen Germany Univ Klinikum Dusseldorf Dept Orthodont D-40225 Dusseldorf Germany

Purpose Volumetric quantitative analyses of bone micromorphometry changes following orthodontic tooth movements are hardly standardizable. The present study aimed at validating and applying a novel microcomputed tomography (CT)-based approach that enables the segmentation of teeth and definition of a standardized volume of interest (VOI) around the roots to assess local bone micromorphometry. Methods The jaws of 3 untreated and 14 orthodontically treated mice (protraction of the upper right molar for 11 days with 0.5 N;untreated left upper molar) were scanned with a micro-CT. The first molars and the alveolar bone were segmented, and a standardized VOI was defined around the teeth. The bone volume per total volume (BV/TV) was assessed within the VOI, and BV/TV values were compared between contralateral sites in both untreated (method validation) and treated animals (method application). Results The intraclass correlation coefficient of 0.99 revealed high reliability of the method. In the untreated animals, Bland-Altman analysis confirmed comparable BV/TV fractions (mean difference: -1.93, critical difference: 1.91, Wilcoxon: p = 0.03). In the orthodontically treated animals, BV/TV values were significantly lower at the test compared to the control site (test: 33.23% +/- 5.74%, control: 41.33% +/- 4.91%, Wilcoxon: p < 0.001). Conclusion Within the limits of the study, the novel approach demonstrated the applicability to evaluate bone micromorphometry around teeth subjected to orthodontic treatment.

关键词： watershed algorithm Periodontal bone analysis Microcomputed tomography Tooth protraction Volumetric analysis

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