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grabcut algorithm for dental X-ray images based on full threshold segmentation

IET IMAGE PROCESSING 2018年第12期12卷 2330-2335页

作者： Mao, Jiafa Wang, Kaihui Hu, Yahong Sheng, Weiguo Feng, Qixin Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Hangzhou Normal Univ Dept Comp Sci Hangzhou 311121 Zhejiang Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi 830011 Xinjiang Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Teeth are difficult to be destroyed due to their corrosion resistance, high melting point and hardness. Dental biometrics can therefore provide assistance in human forensic identification, especially to the unknown corpses. One of the key issue in dental based human identification is the segmentation of Dental X-ray images. In this paper, a novel segmentation algorithm has been proposed for this purpose. The proposed algorithm is based on full threshold segmentation. We first obtain the outline image set Iwhole(n) and crown image set Icrown(m) of the complete target tooth. Morphological open operation is then applied to the difference images of Iwhole(n) and Icrown(m). Subsequently, the most complete target tooth image and its corresponding crown image are selected. Getting independent target tooth image I-contour and its crown image I-crown from these two images. Median filtering is applied to the synthetic image of I-contour and I-crown, and the resulted image will be used as the Mask for grabcut to obtain the target tooth image. Experimental results show our proposed algorithm can effectively overcome the problems of uneven grayscale distribution and adhesion of adjacent crowns in dental X-ray images. It can also achieve a high segmentation accuracy and outperform related methods to be compared.

关键词： feature extraction biometrics (access control) dentistry medical image processing image segmentation grabcut algorithm dental X-ray images threshold segmentation high melting point hardness dental biometrics human forensic identification dental-based human identification novel segmentation algorithm difference images complete target tooth image corresponding crown image independent target tooth shape image synthetic image resulted image adjacent dental crowns high segmentation accuracy outline image set median filtering morphological open operation

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Lightweight Plant Disease Classification Combining grabcut algorithm, New Coordinate Attention, and Channel Pruning

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NEURAL PROCESSING LETTERS 2022年第6期54卷 5317-5331页

作者： Qi, Fang Wang, Yongle Tang, Zhe Cent South Univ Sch Comp Sci Changsha 410083 Hunan Peoples R China

In this paper, a lightweight convolutional neural network is proposed for the classification of plant diseases, containing 63 classes of states for 11 plant species. The different context of experimental data and data in the real environment, insufficient accuracy of the model classification, and oversized model are three main problems of deep learning techniques applied to agricultural production. In this paper, we mainly focus on these three problems. First, the grabcut algorithm is adopted to unify the background of the experimental data and the real data to black, allowing the trained model to have the same good effect when applied in practice. Then, we propose a new coordinate attention block to improve the classification accuracy of convolutional neural networks and empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models. Finally, channel pruning is applied to the trained model, which reduces the model size and computational effort by 85.19% and 92.15% respectively with little change in the model accuracy, making it better suited for agricultural platforms with lower memory and computational capacity.

关键词： Convolutional neural networks Plant diseases grabcut algorithm New coordinate attention block Channel pruning

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Optimizing Skin Cancer Diagnosis: A Modified Ensemble Convolutional Neural Network for Classification

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MICROSCOPY RESEARCH AND TECHNIQUE 2025年第0期 2025 Jan 31页

作者： Vidhyalakshmi, A. M. Kanchana, M. SRM Inst Sci & Technol Dept Comp Technol Kattankulathur Tamilnadu India

Skin cancer is recognized as one of the most harmful cancers worldwide. Early detection of this cancer is an effective measure for treating the disease efficiently. Traditional skin cancer detection methods face scalability challenges and overfitting issues. To address these complexities, this study proposes a random cat swarm optimization (CSO)with an ensemble convolutional neural network (RCS-ECNN) method to categorize the different stages of skin cancer. In this study, two deep learning classifiers, deep neural network (DNN) and Keras DNN (KDNN), are utilized to identify the stages of skin cancer. In this method, an effective preprocessing phase is presented to simplify the classification process. The optimal features are selected using the feature extraction phase. Then, the grabcut algorithm is employed to carry out the segmentation process. Also, the CSO is employed to enhance the effectiveness of the method. The HAM10000 and ISIC datasets are utilized to evaluate the RCS-ECNN method. The RCS-ECNN method achieved an accuracy of 99.56%, a recall of 99.66%, a specificity value of 99.254%, a precision value of 99.18%, and an F1-score value of 98.545%, respectively. The experimental results demonstrated that the RCS-ECNN method outperforms the existing techniques.

关键词： cat swarm optimization deep neural network grabcut algorithm Keras deep neural network skin cancer

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A training-free machine learning approach for 3D powder bed reconstruction and defect detection in PBF-LB/M

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PROGRESS IN ADDITIVE MANUFACTURING 2025年 1-25页

作者： Tosoratti, Enrico Potthoff, Ugne Bennewitz, Christopher Bambach, Markus Inspire AG Innovat Ctr Addit Mfg Switzerland CH-9014 St Gallen Switzerland Swiss Fed Inst Technol Adv Mfg Lab D MAVT Technoparkstr 1 CH-8005 Zurich Switzerland

We introduce a novel in situ quality monitoring approach for the Laser Powder Bed Fusion (PBF-LB/M) process that operates independently of prior experimental data and can be integrated into existing systems. We employ a cost-effective, high-resolution CMOS camera coupled with a bright and darkfield lighting arrangement to acquire data layer-by-layer. Using computer vision techniques, specifically the grabcut algorithm, we automate the inspection and the virtual reconstruction of fused part geometries. Our results, compared with nominal slice data and computed tomography scan data, indicate a robust accuracy in quantifying geometric deviations (mean deviation of 15.60 mu\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}m and 15.20 mu\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document}m respectively, Jaccard scores of 0.99). The proposed training-free framework, unlike traditional machine learning methods, requires no labeled datasets or prior training, offering a cost-effective and adaptable solution. This eliminates the dependency on material, scan strategy, or part geometry, which typically hinders the scalability of conventional approaches. Additionally, our method facilitates automatic defect detection such as recoater strikes, recoater hopping, and powder shortages, with balanced accuracy scores of up to 0.87. These advantages highlight the framework's potential as a practical tool for in situ process monitoring and quality assurance in PBF-LB/M systems.

关键词： PBF-LB/M Powder bed quality inspection Fused part geometry inspection Image segmentation Computer vision grabcut algorithm

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Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and grabcut

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IET IMAGE PROCESSING 2016年第12期10卷 937-942页

作者： Fu, Ruigang Li, Biao Gao, Yinghui Wang, Ping Natl Univ Def Technol ATR Key Lab Changsha Hunan Peoples R China

Figure-ground segmentation is used to extract the foreground from the background, where the foreground is usually defined as the region containing the most meaningful object of the image. In fact, the algorithms that take advantage of humancomputer interaction often attain better performance and they are based on the 'one-to-one' model. In this study, the authors present a novel algorithm for figure-ground segmentation based on the grabcut algorithm, which is a common segmentation algorithm that is user interactive. However, instead of a real user, they attempt to use a pre-trained deep convolutional neural network to interact with grabcut for completing its job successfully. Weizmann's segmentation evaluation database is used as the test dataset and the results show that their algorithm works well for figure-ground segmentation. While the previous automatic segmentation algorithms are required to rank their segments empirically in order to find the position of the foreground after the segmentation, their algorithm is fully automatic.

关键词： image segmentation convolution neural nets feature extraction human computer interaction deep convolutional neural network grabcut algorithm image object human–computer interaction fully automatic figure-ground segmentation algorithm Weizmann segmentation evaluation database

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Segmentation method for medical image based on improved grabcut

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2017年第4期27卷 383-390页

作者： Lu, Yu-Wei Jiang, Jian-Guo Qi, Mei-Bin Zhan, Shu Yang, Jie Hefei Univ Technol Sch Comp & Informat 193 Tunxi Rd Hefei 230009 Anhui Peoples R China Anhui Univ Tradit Chinese Med Sch Integrated Tradit & Western Med Dept Publ Hlth & Gen Med 103 Meishan Rd Hefei 230038 Anhui Peoples R China

Segmentation of medical images has a lot of interferences because of the low contrast and fuzzy boundaries. It's hard to get perfect effect using present image segmentation methods, so we put forward an improved algorithm based on grabcut and Gaussian mixture model (GMM) in this paper in order to obtain simplify interactive operation and better segmentation precision. We extend the grabcut approach in 2 respects. Firstly, the initial GMMs of foreground and background were obtained by training sets, which could improve the algorithm's convergence rate. Secondly, the segmentation was restricted by the figure of foreground from training. Experimental results showed that compared with the traditional grabcut algorithm, our proposed algorithm can simplify interactive operation (t=14.33, P<.01) and improve the segmentation speed (t=16.77, P<.01). In addition, in respect of segmentation precision, our proposed algorithm was obviously better than the traditional algorithms such as Graph Cut, grabcut and Lazy Snapping. (F=149.546, P<.01). The improved algorithm we proposed in this manuscript is especially suitable for processing large-scale medical images.

关键词： figure of foreground Gaussian Mixture model grabcut algorithm image segmentation training set

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Novel Contour Based Detection and grabcut Segmentation for Sign Language Recognition 2

Novel Contour Based Detection and GrabCut Segmentation for S...

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2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Kumar, Malladi Sai Phani Lathasree, Veerapalli Karishma, S. N. IIT Kharagpur ATDC Kharagpur 721302 W Bengal India Infosys Ltd Hyderabad 500045 Andhra Prades India IIT Kharagpur Dept E&ECE Kharagpur 721302 W Bengal India

ISBN: (纸本)9781509044429

The main intention of this paper is to build an automatic computer aided hand gesture to voice conversion system for people suffering from Aphonia, a medical term for speech impairment. We have assumed the input gesture images given to the system as simple and complex depending on the background of the image. A novel contour based image segmentation algorithm is proposed in this paper to detect the boundary of the foreground from images with simple dark background. The traditional grabcut algorithm is employed for segmentation of foreground from images with complex background. This algorithm iteratively segments the image to extract the foreground accurately. The American Sign Language (ASL) 26 finger-spelled alphabet images are taken as the dataset for the two above mentioned algorithms. For the dataset that we have generated, it is observed from the results that contour based segmentation algorithm provides absolutely perfect results. The number of iterations required for grabcut algorithm to segment the foreground may vary from image to image depending on the background. Out of all the 26 gesture of alphabets, Q, R and S need 6 number of iterations at maximum. A minimum of 1 iteration is required for alphabets E, J and O. On an average, 3 iterations of grabcut algorithm is required to completely segment the foreground from images with complex background.

关键词： complex background contour based detection grabcut algorithm gesture segmentation American Sign Language

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An Improved Video Matting Technique Based on grabcut

An Improved Video Matting Technique Based on GrabCut

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3rd International Conference on Digital Image Processing (ICDIP 2011)

作者： Yang, Lei Ma, Chunxi Wu, Xiaoyu Li, Shaobin Wang, Xiaomeng Commun Univ China Digital Media Dept Beijing Peoples R China Commun Univ China Digital Media Dept Beijing Peoples R China Commun Univ China Televis Engn Dept Beijing Peoples R China Commun Univ China Radio & Televis Engn Dept Beijing Peoples R China

ISBN: (纸本)9780819485830

Now grabcut algorithm is a comparatively ideal interactive image segmentation method, and it has the advantages of high precision segmentation and low interaction. But so far grabcut operator is an interactive segmentation method only be used in static images. To solve this problem, this paper proposes a method of video matting, which is based on the grabcut operator. This method is going to achieve video auto-mating by transmitting the matting result of a frame to the next frame. And this method improves the processing speed. By experiments, this method demonstrates a wonderful segmentation result.

关键词： grabcut algorithm Interactive image segmentation Matting technology

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A new video segmentation approach: grabcut in local window

A new video segmentation approach: GrabCut in local window

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2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011

作者： Yang, Lei Guo, Yiming Wu, Xiaoyu Wang, Xiaomeng Digital Media Department Communication University of China Beijing China Radio and Television Engineering Department Communication University of China Beijing China

ISBN: (纸本)9781457711947

grabcut is a classical efficient algorithm used in image segmentation. In this paper we provide an approach of video segmentation-an algorithm which is called "grabcut in local window algorithm" based on grabcut algorithm. First, make cutout with grabcut in series ofoverlap local windows and get corresponding local contour boundaries. Then the contour boundaries in local windows are linked to get foreground contour boundary. Finally foreground segmentation result is obtained by filling the global contour boundary. The comparison with the original grabcut algorithm is demonstrated at the end of this paper, proving the method is efficient. © 2011 IEEE.

关键词： algorithm design and analysis Data models Dilation Educational institutions Equations Erosion grabcut grabcut algorithm Image color analysis Image segmentation Local Window Minimization Video Segment image segmentation image segmentation local contour bou

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Improving object segmentation by using EEG signals and rapid serial visual presentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第22期74卷 10137-10159页

作者： Mohedano, Eva Healy, Graham McGuinness, Kevin Giro-i-Nieto, Xavier O'Connor, Noel E. Smeaton, Alan F. Dublin City Univ Insight Ctr Data Analyt Dublin 9 Ireland Univ Politecn Cataluna Image Proc Grp Catalunya Spain

This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from 0.47 to 0.66 when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score.

关键词： Brain-computer interfaces Electroencephalography Rapid serial visual presentation Object segmentation Interactive segmentation grabcut algorithm

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