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A new approach for automated image segmentation based on simplified PCNN

Computer Aided Drafting,Design and Manufacturing 2013年第1期23卷 21-26页

作者： ZHENG Qianqian SHU Zhibiao College of Mathematics and Computer Science Fuzhou University

Pulse-coupled neural network （PCNN） is a novel neural network, which has been widely used in image segmentation. However, there are still some limitations, such as trial-and-error parameter settings and manual selection of the optimal results. This paper puts forward a new method based on the simplified PCNN model for automatic image segmentation. By calculating the un- iformity measure of the corresponding image at each process of iteration, the optimal segmentation result is obtained when the max- imum value of the uniformity measure is achieved. Experimental results show that the proposed method can automatically achieve better segmentation result and has a common adaptability.

关键词： pulse-coupled neural network automated image segmentation uniformity measure

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Automatic segmentation of Quadriceps Femoris Cross-Sectional Area in Ultrasound images: Development and Validation of Convolutional Neural Networks in People With Anterior Cruciate Ligament Injury and Surgery

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ULTRASOUND IN MEDICINE AND BIOLOGY 2025年第2期51卷 364-372页

作者： Tayfur, Beyza Ritsche, Paul Sunderlik, Olivia Wheeler, Madison Ramirez, Eric Leuteneker, Jacob Faude, Oliver Franchi, Martino V. Johnson, Alexa K. Palmieri-Smith, Riann Univ Michigan Sch Kinesiol Ann Arbor MI USA Univ Michigan Orthoped Rehabil & Biomech ORB Lab Ann Arbor MI USA Michigan Med Dept Orthopaed Surg Ann Arbor MI USA Univ Basel Dept Sport Exercise & Hlth Basel Switzerland Univ Padua Dept Biomed Sci Padua Italy

Objective: Deep learning approaches such as DeepACSA enable automated segmentation of muscle ultrasound cross-sectional area (CSA). Although they provide fast and accurate results, most are developed using data from healthy populations. The changes in muscle size and quality following anterior cruciate ligament (ACL) injury challenges the validity of these automated approaches in the ACL population. Quadriceps muscle CSA is an important outcome following ACL injury;therefore, our aim was to validate DeepACSA, a convolutional neural network (CNN) approach for ACL injury. Methods: Quadriceps panoramic CSA ultrasound images (vastus lateralis [VL] n = 430, rectus femoris [RF] n = 349, and vastus medialis [VM] n = 723) from 124 participants with an ACL injury (age 22.8 +/- 7.9 y, 61 females) were used to train CNN models. For VL and RF, combined models included extra images from healthy participants (n = 153, age 38.2, range 13-78) that the DeepACSA was developed from. All models were tested on unseen external validation images (n = 100) from ACL-injured participants. Model predicted CSA results were compared to manual segmentation results. Results: All models showed good comparability (ICC > 0.81, < 14.1% standard error of measurement, mean differences of <1.56 cm(2)) to manual segmentation. Removal of the erroneous predictions resulted in excellent comparability (ICC > 0.94, < 7.40% standard error of measurement, mean differences of <0.57 cm(2)). Erroneous predictions were 17% for combined VL, 11% for combined RF, and 20% for ACL-only VM models. Conclusion: The new CNN models provided can be used in ACL-injured populations to measure CSA of VL, RF, and VM muscles automatically. The models yield high comparability to manual segmentation results and reduce the burden of manual segmentation.

关键词： Anterior cruciate ligament reconstruction ACLR automated image segmentation Deep neural networks Panoramic ultrasound Quadriceps femoris muscle Muscle cross sectional area

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Bubble evolution identification method based on U-Net deep learning

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SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Gou, Yuxin Shi, Dongyan Wang, Jiuqiang Zhang, Haifeng Cui, Xiongwei Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Engn Univ Coll Shipbldg Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

This study addresses the challenge of automated analysis of underwater bubble dynamics, as traditional methods for bubble detection often struggle with precision and real-time processing, requiring labour-intensive manual analysis or inefficient image processing techniques. To overcome these limitations, we propose a U-Net deep learning-based method for bubble segmentation and dynamic analysis, utilising high-speed photography (62420 fps) to capture bubble formation and dissipation. The proposed method achieves accurate bubble segmentation through deep feature extraction and upsampling using the U-Net model, with a proportional estimation method applied to calculate bubble size, expansion rate, and movement rate. Validation through manual measurements and CFD simulations confirms the method's reliability. The results demonstrate that the U-Net model enhances the efficiency and accuracy of bubble identification, providing a robust tool for studying underwater bubble dynamics in real-time.

关键词： U-net deep learning bubble evolution identification underwater electric spark experiment expansion and movement rates automated image segmentation

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automated grain boundary detection using the level set method

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COMPUTERS & GEOSCIENCES 2009年第2期35卷 267-275页

作者： Lu, Bibo Cui, Min Liu, Qiang Wang, Yangang Jilin Univ Coll Math Changchun 130012 Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454000 Peoples R China China Univ Petr Basin & Reservoir Res Ctr Beijing 102249 Peoples R China Chinese Acad Sci Comp Network Informat Ctr Supercomp Ctr Beijing 100190 Peoples R China

In this paper, we detect the grain boundary by using a novel automated algorithm which is based on the level set method (the LSM). The LSM starts with only one initial closed curve which can be arbitrary in both shape and location in the microphotograph. The curve splits and merges repeatedly until it reaches the boundaries of the objects. The evolution depends on the structure of the image. This method requires simple and feasible constraints which ensure the exclusion of the objects unsuitable for reliable measurements and strain analysis. The LSM has the following three advantages: (1) with a single image as input, segmentation is automated without human intervention, (2) the grain boundary detected by the LSM is a closed curve, and (3) simple post-processing maintains the shape of the boundary. The segmentation and strain analysis by the LSM are demonstrated and the results are compared with those from hand-drawn method. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Grain boundary detection The level set method automated image segmentation Strain analysis

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Towards automated enhancement, segmentation and classification of digital brain images using networks of networks

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INFORMATION SCIENCES 2001年第1-4期138卷 45-77页

作者： Sha, DD Sutton, JP MIT Harvard MIT Div Hlth Sci & Technol Cambridge MA 02139 USA Massachusetts Gen Hosp Neural Syst Grp Charlestown MA 02129 USA Harvard Univ Charlestown MA 02129 USA

An adaptive image processing algorithm (IEEE Trans. Neural Networks 8 (1997) 169), based on biological principles, is extended and applied to enhance, segment and classify digital brain images acquired using magnetic resonance imaging (MRI). The algorithm is based on the network of networks (NoN) neural computing theory (World Cong. Neural Networks 1 (1995) 561). The initial algorithm, developed by Guan and Sutton (GS), uses vector connections among model neurons to delineate regions of pixels that have dynamic boundaries, corresponding to critical features of images. The boundaries subdivide large regions into smaller regions, where the smaller regions operate as gradient descent networks to enhance local aspects of blurred images. In this report, the authors develop and test two new algorithms that build upon the success of the GS algorithm. The first algorithm, termed the segmentation-variance (Sv) algorithm, maps pixels into discrete groups using a segmentation function of the local variance. Boundaries formed at the transition zones among groups are subsequently tuned to automatically delineate brain regions of interest. This is demonstrated using MRI scans of the human brain. In a second algorithm, pixel groupings formed using the SV algorithm are consolidated into larger groups to generate histograms. Dynamic maps and histograms capture features of object relationships and are used to classify structures. The resultant algorithm is called the dynamic segmentation and classification (DSC) algorithm. It is tested on MRI scans of four geometric patterns under various noise conditions. The SV and DSC algorithms illustrate proof of principal concepts in the evolution towards automated segmentation and classification of digital brain images. (C) 2001 Elsevier Science Inc. All rights reserved.

关键词： neural networks image enhancement automated image segmentation automated image classification hierarchical networks dynamic clustering pattern recognition

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automated COMPUTED TOMOGRAPHY-ULTRASOUND CROSS-MODALITY 3-D CONTOURING ALGORITHM FOR PROSTATE

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ULTRASOUND IN MEDICINE AND BIOLOGY 2015年第10期41卷 2646-2662页

作者： Ermacora, Denis Pesente, Silvia Pascoli, Francesco Raducci, Sebastian Mauro, Rudy Abu Rumeileh, Imad Verhaegen, Frank Fontanarosa, Davide Datamind Udine Italy Tecnol Avanzate Srl Turin Italy CRO Ist Nazl Tumori Dept Radiat Oncol Aviano PN Italy Maastricht Univ Med Ctr GROW Sch Oncol & Dev Biol Dept Radiat Oncol MAASTRO NL-6201 BN Maastricht Netherlands Philips Res Oncol Solut Dept NL-5656 AE Eindhoven Netherlands

A novel fully automated algorithm is introduced for 3-D cross-modality image segmentation of the prostate, based on the simultaneous use of co-registered computed tomography (CT) and 3-D ultrasound (US) images. By use of a Gabor feature detector, the algorithm can outline in three dimensions and in cross-modality the prostate, and it can be trained and optimized on specific patient populations. We applied it to 16 prostate cancer patients and evaluated the conformity between the automatically segmented prostate contours and the contours manually outlined by an experienced physician, on the CT-US fusion, using the mean distance to conformity (MDC) index. When only the CT scans were used, the average MDC value was 4.5 +/- 1.7 mm (maximum value - 9.0 mm). When the US scans also were considered, the mean +/- standard deviation was reduced to 3.9 +/- 0.7 mm (maximum value = 5.5 mm). The cross-modality approach acted on all the largest distance values, reducing them to acceptable discrepancies. (E-mail: ***@***) (C) 2015 World Federation for Ultrasound in Medicine & Biology.

关键词： Ultrasound imaging Computed tomography imaging automated image segmentation Cross-modality Radiotherapy image-guided radiation therapy

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The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning

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EXPERT REVIEW OF MEDICAL DEVICES 2018年第5期15卷 349-356页

作者： Huff, Trevor J. Ludwig, Parker E. Zuniga, Jorge M. Creighton Univ Sch Med Omaha NE 68108 USA Univ Nebraska Dept Biomech Omaha NE 68182 USA Univ Autonoma Chile Fac Ciencias Salud Providencia Chil Chile

Introduction: 3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are *** covered: This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time *** commentary: ML-based segmentation of medical images could potentially improve the process of 3D anatomical modeling. However, until more research is done to validate these technologies in clinical practice, their impact on patient outcomes will remain unknown. We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data.

关键词： 3D manufacturing 3D printing additive manufacturing anatomical modeling artificial intelligence automated image segmentation computer-aided manufacturing convolutional neural network machine learning medical image segmentation personalized medicine surgical model surgical planning three-dimensional printing

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Automatic image-based determination of pruning mass as a determinant for yield potential in grapevine management and breeding

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AUSTRALIAN JOURNAL OF GRAPE AND WINE RESEARCH 2017年第1期23卷 120-124页

作者： Kicherer, A. Klodt, M. Sharifzadeh, S. Cremers, D. Toepfer, R. Herzog, K. Julius Kuhn Inst Fed Res Ctr Cultivated Plants Inst Grapevine Breeding Geilweilerhof D-76833 Siebeldingen Germany Tech Univ Munich Dept Comp Sci D-85748 Garching Germany Univ Oxford Math Inst Radcliffe Observ Quarter Oxford England

Background and Aims: Vine balance is defined as a relation between vegetative (mass of dormant pruning wood) and generative (yield) growth. For grapevine breeding, emphasis is usually placed on the evaluation of individual seedlings. In this study, we calculated the mass of dormant pruning wood with the assistance of an automated image-based method for estimating the pixel area of dormant pruning wood. The evaluation of digital images in combination with depth map calculation and image segmentation is a new and non-invasive tool for objective data acquisition. Methods and Results: The proposed method was tested on a set of seedlings planted at the Institute for Grapevine Breeding Geilweilerhof, Germany. All images taken in the field were geo-referenced, and the automated method was validated by manual segmentation. Together with additional yield parameters, the vine balance indices can be used to classify seedlings for breeding purposes. Conclusion: The computed pruning mass obtained using image-based methods is an accurate, inexpensive and easy method to estimate pruning mass compared with the manual time-consuming measurements. Together with the yield parameters, it is a suitable method for seedling evaluation and can also be used in precision viticulture. Significance of the Study: This study demonstrates an image-based evaluation of the pruning mass to be a highly valuable tool for grapevine research and grapevine breeding. Moreover, the tool might be used by industry to monitor vine balance. The key findings reported have the potential to increase grapevine breeding efficiency by using an accurate and objective phenotyping method.

关键词： automated image segmentation computed pruning mass dense depth map grapevine breeding vine balance Vitis vinifera

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Fuzzy clustering algorithm for automatically determining the number of clusters

Fuzzy clustering algorithm for automatically determining the...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Hu Yangyang Liu Zengli Kunming Univ Sci & Technol Informat & Automat Inst Kunming Yunnan Peoples R China

ISBN: (纸本)9781728117089

In the process of image segmentation based on clustering, it is necessary to manually determine the number of clusters segmented, which makes the image automation less. A fuzzy clustering algorithm is proposed to automatically determine the number of clusters. First, the similarity matrix is constructed using the features of the extracted image, and the number of clusters is determined using the AP clustering algorithm. Then, the obtained number of clusters is input to the FCM algorithm, and the clustering center is optimized using the firefly algorithm. Finally, a simulation experiment was conducted. The experimental results show that the improved FCM algorithm has a good effect and can realize automatic image segmentation.

关键词： AP clustering firefly algorithm fuzzy C-means clustering automated image segmentation

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Implementation of automated 3D Defect Detection for Low Signal-to Noise Features in NDE Data

Implementation of Automated 3D Defect Detection for Low Sign...

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10th International Conference on Barkhausen and Micro-Magnetics (ICBM)

作者： Grandin, R. Gray, J. Iowa State Univ Ctr NDE Ames IA 17500 USA

ISBN: (纸本)9780735412125

The need for robust defect detection in NDE applications requires the identification of subtle, low-contrast changes in measurement signals usually in very noisy data. Most algorithms rarely perform at the level of a human inspector and often, as data sets are now routinely 10+ Gigabytes, require laborious manual inspection. We present two automated defect segmentation methods, simple threshold and a binomial hypothesis test, and compare effectiveness of these approaches in noisy data with signal to noise ratios at 1:1. The defect-detection ability of our algorithm will be demonstrated on a 3D CT volume, lIT C-scan data, magnetic particle images, and using simulated data generated by XRSIM. The latter is a physics-based forward model useful in demonstrating the effectiveness of data processing approaches in a simulation which includes complex defect geometry and realistic measurement. These large data sets represent significant demands on compute resources and easily overwhelm typical PC platforms;however, the emergence of graphics processing units(GPU) processing power provides a means to overcome this bottleneck. Processing large, multi-dimensional datasets requires an optimal GNI implementation which addresses both computational complexity and memory-bandwidth usage.

关键词： automated image segmentation Low Signal-to-Noise Low Contrast CPU Computing

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