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Threshold Ranges of Multiphase Components from Natural Ice CT Images Based on watershed algorithm

WATER 2024年第22期16卷 3330页

作者： Hu, Shengbo Wang, Qingkai Li, Chunjiang Li, Zhijun Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Inner Mongolia Univ Sci & Technol Sch Energy & Environm Baotou 014010 Peoples R China

The multiphase components of natural ice contain gas, ice, unfrozen water, sediment and brine. X-ray computed tomography (CT) analysis of ice multiphase components has the advantage of high precision, non-destructiveness and visualization;however, it is limited by the segmentation thresholds. Due to the proximity of the CT value ranges of gas, ice, unfrozen water, sediment and brine within the samples, there is uncertainty in the artificial determination of the CT image segmentation thresholds, as well as unsuitability of the global threshold segmentation methods. In order to improve the accuracy of multi-threshold segmentation in CT images, a CT system was used to scan the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice. The threshold ranges of multiphase components within the ice were determined by watershed algorithm to construct a high-precision three-dimensional ice model. The results indicated that CT combined with watershed algorithm was an efficient and non-destructive method for obtaining microscopic information within ice, which accurately segmented the ice into multiphase components such as gas, ice, unfrozen water, sediment, and brine. The gas CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from -1024 Hu similar to-107 Hu, -1024 Hu similar to-103 Hu, and -1024 Hu similar to-160 Hu, respectively. The ice CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from -103 Hu similar to-50 Hu, -100 Hu similar to-38 Hu, -153 Hu similar to-51 Hu. The unfrozen water CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from -8 Hu similar to 18 Hu, -8 Hu similar to 13 Hu. The sediment CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from 20 Hu similar to 3071 Hu, 20 Hu similar to 3071 Hu, and the brine CT values of the Arctic sea ice ranged from -6 Hu similar to 3071 Hu. The errors between the three-dimensional ice model divided

关键词： threshold ranges natural ice multiphase components CT images watershed algorithm

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Bubble Image Segmentation Based on a Novel watershed algorithm With an Optimized Mark and Edge Constraint

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Peng, Cheng Liu, Yikun Gui, Weihua Tang, Zhaohui Chen, Qing Hunan Univ Technol Sch Comp Zhuzhou 412007 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Bubble size contains important indication information that is closely related to flotation production conditions and process indicators. However, bubble images often have low contrast, noise, and many other shortcomings, making foam segmentation a difficult problem that the existing segmentation methods cannot solve. In this article, an improved watershed algorithm based on optimal labeling and edge constraints is proposed. Three algorithms are designed to obtain different initial tags, and then the extracted content of different tags is fused to obtain the combined foreground tag. To reduce the offset of the segmentation line, the edge operator is applied to extract the bubble boundary, and the boundary priori condition is used as a constraint to correct the segmentation line. Finally, the optimal segmentation line is obtained by fusing foreground markers and external constraints. Industrial experiments show that this method is effective and has a higher accuracy than the other methods. The average value and variance of rand index (RI) are 92.88 & x0025;and 0.69, respectively.

关键词： Image segmentation Image edge detection Filtering Kernel Interference Electronic mail Classification algorithms Bubble image edge constraint image segmentation watershed algorithm

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Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging

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PHYSICS AND CHEMISTRY OF THE EARTH 2021年 123卷 103016-103016页

作者： Cao, Wenxuan Qiao, Zeyu Gao, Zeyu Lu, Saihong Tian, Fei China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China Minist Agr & Rural Affairs Wuwei Expt Stn Efficient Water Use Agr Wuwei 733000 Peoples R China

Lodging is a major yield-limiting factor in wheat, so we need a method to collect wheat lodging information efficiently. Traditional extraction of crop lodging is cost intensive and time consuming;satellite remote sensing technology is vulnerable to the cloud and is limited by its temporal and spatial resolution. Therefore, a new method for wheat lodging extraction based on a watershed algorithm combined with an adaptive threshold segmentation hybrid algorithm is proposed. An unmanned aerial vehicle equipped with a high-resolution thermal infrared and visible sensor was used to acquire images of lodged and nonlodged wheat. Results indicate that there are morphological differences between lodged and nonlodged wheat and that the spectral reflectance and temperature of lodged wheat is higher than that of nonlodged wheat. The watershed algorithm is sensitive to the edge response and can reflect the contour characteristics of the lodging area. However, the impact of noise is obvious;there are salt-and-pepper patches. The wheat lodging ratios were calculated as 17.81%, with an accuracy of 80.26%. With adaptive threshold segmentation, the wheat lodging ratios were calculated as 18.78%, with an accuracy of 84.65%. If these two methods are fused, the influence of noise can be reduced, and an extraction accuracy of 93.58% can be achieved. This research provides a new idea for highly efficient crop lodging data collection, which can be used for precision farming and the selection of crop genotypes with high lodging resistance.

关键词： Wheat lodging watershed algorithm Adaptive threshold segmentation Hybrid algorithm UAV

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Human Skin Region Segmentation Based on Chrominance Component Using Modified watershed algorithm

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Procedia Computer Science 2016年 89卷 856-863页

作者： Alak Das Dibyendu Ghoshal Government Degree College Longtarai Valley Chailengta Dhalai Tripura 799 273 India National Institute of Technology Agartala Jirania West Triura Tripura 799 046 India

A novel watershed segmentation algorithm has been proposed to segment the human skin region of RGB color images based on Cb component of YCbCr color space in this paper. The conventional watershed segmentation algorithm is not suitable for segmentation of human skin region from color images directly, since it is difficult to decide suitable color information of human skin. At first stage, the RGB image is converted into YCbCr color space which contains three components luminance, blue difference component (Cb) and red-difference component (Cr). The Cb component has been extracted and used for further processing steps. After extraction of Cb component, the modified marker based watershed algorithm has been applied for segmentation of human skin region only from RGB color image. The proposed method has been tested on FRI CVL Face Database. In experimental stage, results show that the proposed technique avoids over segmentation problem and produce good segmentation result which is comparable with the same obtained by other algorithms.

关键词： Cb Component Cr Component Segmentation Skin Region watershed algorithm

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A Marker-Controlled watershed algorithm for the Intelligent Picking of Long Jujubes in Trees

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FORESTS 2022年第7期13卷 1063页

作者： Dai, Yingpeng Meng, Lingfeng Wang, Songfeng Sun, Fushan Chinese Acad Agr Sci Tobacco Res Inst Qingdao 266000 Peoples R China

Vision is the most important way for an unmanned picking or plant protection robot to navigate an external environment. To achieve intelligent picking or plant protection, it is essential to obtain target location information. A new marker-controlled watershed (MCW-D) algorithm is proposed for object segmentation. By analyzing the shortcomings of the watershed algorithm and the characteristics of objects, the proposed MCW-D method mainly solves three problems. First, it reduces the influence of shadow and other factors on image color information. Based on histogram specification, secondary mapping is used to reduce the effects of lighting. Second, marker images are selected. All points with markers need to be located in the target object. The hue feature of long jujubes and trees is used as the marker image. Third, a mask image is acquired, which requires a clear boundary between the target and the background. An adaptive angle rotation based on an energy-driven approach is designed to find large differences between the target and the background. In a natural environment, the proposed MCW-D method respectively achieves segmentation accuracies of 94.7% and 93.2% on a jujube dataset and a tree dataset, which exceed the accuracies of widely used machine learning methods. These results promote the development of the forest and fruit economies.

关键词： watershed algorithm segmentation agriculture and forestry

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Image Segmentation Using an Improved watershed algorithm

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Journal of Shanghai Jiaotong university(Science) 2004年第2期9卷 16-19页

作者：郭礼华李建华杨树堂陆松年 School of Electronic & Information Technology

As watershed algorithm suffers from over-segmentation problem, this paper presented an efficient method to resolve this problem. First, pre-process of the image using median filter is made to reduce the effect of noise. Second, watershed algorithm is employed to provide initial regions. Third, regions are merged according to the information between the region and boundary. In the merger processing based on the region information, an adaptive threshold of the difference between the neighboring regions is used as the region merge criteria, which is based on the human visual character. In the merger processing on the boundary information, the gradient is used to judge the true boundary of the image to avoid merging the foreground with the background regions. Finally, post-process to the regions using mathematical morphology open and close filter is done to smooth object boundaries. The experimental results show that this method is very efficient.

关键词： image segmentation region merger watershed algorithm

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Application of an improved watershed algorithm based on distance map reconstruction in bean image segmentation

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HELIYON 2023年第4期9卷 e15097页

作者： Liu, Hongquan Zhang, Weijin Wang, Fushun Sun, Xiaohua Wang, Junhao Wang, Chen Wang, Xinxin Hebei Agr Univ Coll Urban & Rural Construction Baoding 071000 Peoples R China Hebei Agr Univ Coll Informat Sci & Technol Baoding 071000 Peoples R China Hebei Key Lab Agr Big Data Baoding 071000 Peoples R China Hebei Software Inst Dept Digital Media Baoding 071000 Peoples R China Agr Technol Innovat Ctr Mountainous Areas Hebei Pr Baoding 071000 Peoples R China Agr Engn Technol Res Ctr Natl North Mountainous Ar Baoding 071000 Peoples R China Hebei Agr Univ Hebei Mt Res Inst Baoding 071000 Peoples R China

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven grey level of the original image, traditional watershed algorithms generate many incorrect edges, resulting in oversegmentation and undersegmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the grey distribution characteristics of the image, the grey reduction amplitude h was selected to generate the mask image with the same grey distribution trend as that of the original image. The original greyscale map was reconstructed with corresponding thresholds selected according to the false minima of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean (Vigna angularis L.) image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of the watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, w

关键词： Image segmentation watershed algorithm Bean image Seed phenotype

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CHRONIC LYMPHOCYTIC LEUKEMIA CELL SEGMENTATION FROM MICROSCOPIC BLOOD IMAGES USING watershed algorithm AND OPTIMAL THRESHOLDING

CHRONIC LYMPHOCYTIC LEUKEMIA CELL SEGMENTATION FROM MICROSCO...

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IEEE Canadian Conference on Electrical and Computer Engineering

作者： Emad A. Mohammed Mostafa M. A. Mohamed Christopher Naugler Behrouz H. Far Electrical and Computer Engineering Schulich School of Engineering University of Calgary Calgary AB Canada Biomedical Engineering Faculty of Engineering Helwan University Cairo Egypt Department of Pathology and Laboratory Medicine University of Calgary and Calgary Laboratory Services Calgary AB Canada

ISBN: (纸本)9781479900312

Chronic lymphocytic leukemia (CLL) is the most common type of blood cancer in Canadian adults. CLL cells are abnormal lymphocytes, which tend to be slightly larger than normal resting lymphocytes and have a condensed appearance to their chromatin. There is a low number of related works on this disease. This paper presents a method to segment normal and CLL lymphocytes into two parts: nucleus, and cytoplasm using a watershed algorithm and optimal thresholding. The goal of this work is reducing the over and under segmentation error of the watershed algorithm by suppressing 1% of the local minima. We tested 140 microscopic lymphocyte images (normal and CLL), and the algorithm obtained 99.92% maximum accuracy for nucleus segmentation, and 99.85% maximum accuracy for cell segmentation. The cytoplasm can be extracted with a 99.63% maximum accuracy with simple mask subtraction. The code for the presented algorithm is shared on the MATLAB file exchange website.

关键词： Chronic Lymphocytic Leukemia (CLL) Segmentation WBC watershed algorithm Optimal Thresholding

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Lipid droplet recognition based on watershed algorithm and convolutional neural network

Lipid droplet recognition based on watershed algorithm and c...

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作者： Shiwei Li Shiqun Yin Haibo Deng College of Computer and Information Science Southwest University

Unbalanced storage and utilization of lipids in the liver can easily lead to non-alcoholic fatty liver,obesity and metabolic ***,it is very significant to detect and classify lipids in cell pathology *** order to achieve accurate identification of lipid droplets,we improved the watershed algorithm to achieve the segmentation of lipid droplets,and classified the lipid droplets based on transfer learning through a convolutional neural *** experiment shows that the improved watershed algorithm is used to segment the lipid droplets and has achieved good *** convolutional neural network transfer learning has achieved a classification accuracy of about 99%.

关键词： watershed algorithm convolutional neural networks Lipid droplet transfer learning

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Multiple Sclerosis lesions detection by a hybrid watershed-Clustering algorithm

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CLINICAL IMAGING 2021年 72卷 162-167页

作者： Bonanno, Lilla Mammone, Nadia De Salvo, Simona Bramanti, Alessia Rifici, Carmela Sessa, Edoardo Bramanti, Placido Marino, Silvia Ciurleo, Rosella IRCCS Ctr Neurolesi Bonino Pulejo SS 113Via PalermoCda Casazza I-98124 Messina Italy

Background: Computer Aided Diagnosis (CAD) systems have been developing in the last years with the aim of helping the diagnosis and monitoring of several diseases. We present a novel CAD system based on a hybrid watershed-Clustering algorithm for the detection of lesions in Multiple Sclerosis. Methods: Magnetic Resonance Imaging scans (FLAIR sequences without gadolinium) of 20 patients affected by Multiple Sclerosis with hyperintense lesions were studied. The CAD system consisted of the following automated processing steps: images recording, automated segmentation based on the watershed algorithm, detection of lesions, extraction of both dynamic and morphological features, and classification of lesions by Cluster Analysis. Results: The investigation was performed on 316 suspect regions including 255 lesion and 61 non-lesion cases. The Receiver Operating Characteristic analysis revealed a highly significant difference between lesions and nonlesions;the diagnostic accuracy was 87% (95% CI: 0.83-0.90), with an appropriate cut-off of 192.8;the sensitivity was 77% and the specificity was 87%. Conclusions: In conclusion, we developed a CAD system by using a modified algorithm for automated image segmentation which may discriminate MS lesions from non-lesions. The proposed method generates a detection out-put that may be support the clinical evaluation.

关键词： watershed algorithm Image segmentation Magnetic Resonance Imaging Multiple Sclerosis CAD system

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