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CT Image Segmentation Method of Composite Material Based on Improved watershed algorithm and U-Net Neural Network Model

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Journal of Shanghai Jiaotong university(Science) 2023年第6期28卷 783-792页

作者：薛永波刘钊李泽阳朱平 State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China School of Design Shanghai Jiao Tong UniversityShanghai 200240China

In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal *** image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material *** study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT *** the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed *** the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation ***,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is *** is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.

关键词： image segmentation composite material segmentation of adhered objects watershed algorithm U-net neural network

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Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm

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ACADEMIC RADIOLOGY 2004年第10期11卷 1125-1138页

作者： Letteboer, MMJ Olsen, OF Dam, EB Willems, PWA Viergever, MA Niessen, WJ Univ Utrecht Ctr Med Image Sci Inst NL-3584 CX Utrecht Netherlands Univ Utrecht Ctr Med Dept Neurosurg NL-3584 CX Utrecht Netherlands IT Univ Copenhagen Dept Innovat Copenhagen Denmark

Rationale and Objective. This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures. Materials and Methods. The watershed method is compared with manual delineation with respect to accuracy, repeatability, and efficiency. Results. In the 20 patients included in this study, the measured volume of the tumors ranged from 2.7 to 81.9 cm(3). A comparison of the tumor volumes measured with watershed segmentation to the volumes measured with manual delineation shows that the two methods are interchangeable according to the Bland and Altman criterion, and thus equally accurate. The repeatability of the watershed method and the manual method are compared by looking at the similarity of the segmented volumes. The similarity for intraobserver and interobserver variability for watershed segmentation is 96.4% and 95.3%, respectively, compared with 93.5% and 90.0% for manual outlining, from which it may be concluded that the watershed method is more repeatable. Moreover, the watershed algorithm is on average three times faster than manual outlining. Conclusion. The watershed method has an accuracy comparable to that of manual delineation and outperforms manual outlining on the criteria of repeatability and efficiency. (C) AUR, 2004.

关键词： brain magnetic resonance imaging (MRI) brain tumors interactive segmentation multiscale image segmentation watershed algorithm

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Image semantics segmentation using watershed algorithm

Image semantics segmentation using watershed algorithm

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IEEE International Conference on Service Operations and Logistics and Informatics

作者： Miao Chengliang Xie Shengli Yu Weiyu South China Univ Technol Inst Elect & Control Guangzhou 510640 Peoples R China

ISBN: (纸本)1424403170

In this paper a novel image semantics segmentation algorithm is proposed, which combines edge and region-merged based techniques. First, an edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of an image gradient. Second, we segment image into primitive regions by applying watershed algorithm on the image gradient magnitude. The watersheds computation algorithm used is based on immersion simulations, that is, on the step of the recursive detection and fast labeling of the different catchment basins using queues. At the end, we merge neighboring region into homologous region using morphological erosion and dilation. Some experiments are presented to illustrate availability and effectiveness of our approach.

关键词： Image Semantics Segmentation gradient computation morphological filter watershed algorithm

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Development of a watershed algorithm for multi-resolution, multi-dimensional clustering of hyperspectral data

Development of a watershed algorithm for multi-resolution, m...

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Conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII

作者： Jellison, GP Hemmer, TH Wilson, DG SITAC Fairfax VA 22033 USA

ISBN: (纸本)0819444758

The watershed-clustering algorithm was adapted for use in multi-dimensional spectral space and was used to define clusters in Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. This algorithm identifies clusters as peaks in a B-dimensional topographic relief, where B is the number of wavelength bands. Image pixel spectra are represented as points in this multi-dimensional space. Analysis is done at increasing values of radiometric resolution, defined by the number of segments into which each wavelength axis is divided. Segmentation of the axes divides the multi-dimensional space into bins, and the number of pixels in each bin is determined. The histogram of the bin populations defines the topography for the watershed analysis. Spectral clusters correspond to mountains or islands on this multi-dimensional surface. The algorithm is analogous to submerging this topography under water, and revealing clusters by determining when mountain peaks appear as the water surface is lowered. Testing of this algorithm reveals some surprising features. Although increasing the radiometric resolution (bins per axis) generally results in large clusters breaking up into greater numbers of small clusters, this is not always the case. Under some circumstances, the separate clusters can recombine into one large cluster when radiometric resolution is increased. This behavior is caused by the existence of single-pixel voxels, which smoothes out the topography, and by the fact that the voxels retain a surprising degree of connectivity, even at high radiometric resolutions. These characteristics of the high-dimensional spectral data provide the basis for further development of the watershed algorithm.

关键词： hyperspectral unsupervised clustering watershed algorithm multi-resolution automated end-member selection HYDICE

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A Method based on watershed algorithm for Core Particles Image Segmentation

A Method based on Watershed Algorithm for Core Particles Ima...

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3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)

作者： Huang Qi-kun Zhou Yun-cai Yangtze Univ Coll Comp Sci Jingzhou Peoples R China

ISBN: (纸本)9781424455379

The watershed algorithm is the method of choice for image segmentation in the field of mathematical morphology, which is run simple, good performance and can better extract the contour of moving object This paper used the watershed algorithm definition by immersion, and modify it, achieved good results after apply to core particles image segmentation. Experiments show that the method of watershed algorithm definition by immersion can meet the core particles image segmentation on both speed and effectiveness.

关键词： watershed algorithm mathematical morphology core particles image image segmentation

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Application of multiresolution, multidimensional clustering of hyperspectral data using the watershed algorithm

Application of multiresolution, multidimensional clustering ...

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Conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII

作者： Hemmer, TH Jellison, GP Wilson, DG SITAC Fairfax VA 22033 USA

ISBN: (纸本)0819444758

In many applications of remotely-sensed imagery, one of the first steps is partitioning the image into a tractable number of regions. In spectral remote sensing, the goal is often to find regions that are spectrally similar within the region but spectrally distinct from other regions. There is often no requirement that these regions be spatially connected. Two goals of this study are to partition a hyperspectral image into groups of spectrally distinct materials, and to partition without human intervention. To this end, this study investigates the use of multi-resolution, multi-dimensional variants of the watershed-clustering algorithm on Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. The watershed algorithm looks for clusters in a histogram: a B-dimensional surface, where B is the number of bands used (up to 210 for HYDICE). The algorithm is applied to HYDICE data of the Purdue Agronomy Farm, for which "ground truth" is available. watershed results are compared to those obtained by using the commonly-available Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.

关键词： hyperspectral unsupervised clustering watershed algorithm multi-resolution automated end-member selection HYDICE

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AUTOMATED GRAIN SIZING USING MARK-BASED watershed algorithm

AUTOMATED GRAIN SIZING USING MARK-BASED WATERSHED ALGORITHM

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chuang, Han-Sheng Lin, Chao-Hung Natl Cheng Kung Univ Dept Geomat Tainan 70101 Taiwan

ISBN: (纸本)9781467311595

This paper presents a method based on mark-based watershed algorithm to automatically extract grains and determine grain sizes from images. Markers generated by the proposed approaches represent rough locations of grains and aperture. Instead of selecting pixels with local minima as marks, we select markers with a priori knowledge, enabling our approach to efficiently ease the problem of over-segmentation in traditional watershed algorithm. The grains patches are extracted correctly after merging the other fragmental patches using both the color and shape properties. Finally, the distribution of grain size is calculated by fitting an ellipse for each detected grain.

关键词： grains sizing watershed algorithm merge

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Research and Implementation of Conglutinated Macrophage Image Segmentation Based on Improved watershed algorithm

Research and Implementation of Conglutinated Macrophage Imag...

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International Conference on Automation, Communication, Architectonics and Materials

作者： Wang, Ping Chen, Rong Luo, Wei Xia, Ruihua Nanchang Univ Dept Elect Informat Engn Nanchang Jiangxi Peoples R China JiangXi Branch China Eastern Airlines JiangXi Peoples R China

ISBN: (纸本)9783037851036

In the process of macrophage image manufacture, cell adhesion often appears because of equipment or man-made reason and then influences subsequent automatic detection and analysis. The traditional watershed algorithm is easy to cause over-segmentation for the volatility of gray extremum. To overcome these phenomena, an improved watershed algorithm which was based on macrophages image segmentation was introduced in this paper. The simulation results show that the improved algorithm can effectively segment adhesive macrophages and restrain the over-segmentation phenomena with an acceptable computation speed.

关键词： distance transformation watershed algorithm image segmentation over-segmentation

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Research on Improved Methods of watershed algorithm

Research on Improved Methods of Watershed Algorithm

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International Conference on Mechatronics, Robotics and Automation (ICMRA 2013)

作者： Wang Deyong Fan Ji Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China Ind Coll Technol Dept Comp Pingdingshan Pingdingshan 467001 Peoples R China

ISBN: (纸本)9783037858066

In this paper an improved image segmentation algorithm based on watershed transform is presented. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameter alpha and beta Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

关键词： image segmentation watershed algorithm normalized cut

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Brain Tumor Segmentation from MR Brain Images using Improved Fuzzy c-Means Clustering and watershed algorithm 5

Brain Tumor Segmentation from MR Brain Images using Improved...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Benson, C. C. Deepa, V. Lajish, V. L. Rajamani, Kumar Univ Calicut Dept Comp Sci Thenhipalam 6733635 Kerala India Robert Bosch Engn & Business Solut Ltd Bangalore 560095 Karnataka India

ISBN: (纸本)9781509020287

Brain is the master and commanding organ of human body. Human brain is affected by many dangerous diseases. Brain tumor or neoplasm is the abnormal growth of tissues in the brain and surrounding regions. MRI is one of the method used for brain tumor diagnosis. Many algorithms are proposed for the automatic extraction of brain tumor tissues from MR brain images. Fuzzy c-Means (FCM) clustering and watershed algorithm are the two commonly used methods for brain tumor extraction. In this paper we implemented the improved version of fuzzy c-Means clustering and watershed algorithm. In fuzzy c-Means clustering we proposed an effective method for the initial centroid selection based on histogram calculation and in watershed algorithm we proposed an atlas based marker detection method for avoiding the over-segmentation problem. Before applying the segmentation algorithms as a pre-processing stage we performed three operations-noise removal, skull stripping and contrast enhancement. We achieved an accuracy of 88.91 and 81.56 of Dice and Tanimoto coefficients for the improved FCM clustering and an accuracy of 93.13 and 88.64 of Dice and Tanimoto coefficients for the improved watershed algorithm.

关键词： Brain Tumor MRI fuzzy c-Means Clustering (FCM) watershed algorithm

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