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IEEE International Conference on image Processing

作者： Serra, J Ecole Mines Paris CMM F-77305 Fontainebleau France

ISBN: (纸本)0780377508

Firstly, the paper proposes an axiomatic definition for the notion of "segmentation" in image processing, which is based on the idea of a maximal partition. Then a key theorem links segmentation with connection, on the one hand, and with connective criteria on the other one. A series of lattice properties are then developed. In a last part, two examples of segmentations are proposed.

关键词： image segmentation

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image segmentation Method for Water Transport Feature Detection in Fabrics via Target-Located Strategy

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IEEE ACCESS 2025年 13卷 48146-48155页

作者： Wang, Yucheng Wang, Huiping Mao, Hang Gao, Suwei Wei, Qiaofeng Li, Shujing Liu, Rangtong Xu, Boyang Zhongyuan Univ Technol Coll Fash Technol Zhengzhou 451191 Henan Peoples R China Collaborat Innovat Ctr Adv Text Equipment Technol Zhengzhou 451191 Henan Peoples R China Zhengzhou Univ Ind Technol Sch Software Zhengzhou 451150 Henan Peoples R China Henan Prov Intelligent Transportat Video Image Per Zhengzhou 450064 Henan Peoples R China

The comfort of apparel, particularly sportswear, is affected by the materials' liquid water transport properties. Many studies have employed machine vision techniques (taking pictures to document changes in the wet zones of fabric) to investigate the transport characteristics of liquid water in fabrics with various topologies. Machine vision techniques fix the problems of manual detection in terms of cost and efficiency. Still, several issues, such as changes in lighting during the detection process, reflections from water droplets, and wrinkles in the fabric, can lower the segmentation accuracy. To easily differ the wet zones from dry parts of fabrics, a novel segmentation and detection method (called SAD) that combines detection models like YOLOv5 with segmentation models like SAM is proposed, after the comparison with SAD to a variety of detection models, including YOLOv5, DETR, and YOLOv3, YOLOv5 presents an optimal accurate. The results show that the accuracy of calculating the area of wet zone in fabric using the SAM-SAD method reaches 94.07%, which is the same as the manual marking method. It is better than traditional models such as Otsu, Canny, and Watershed. And the time-dependent curve is closer to the actual wetting and evaporation process of fabric, the SAD method is conducive to wet zone segmentation.

关键词： Fabrics image segmentation Liquids Object segmentation YOLO Accuracy Gray-scale Feature extraction Manuals Cameras Wet zone segmentation model detection model SAD method liquid water transport

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image segmentation Method Combining Nonlocal Criteria with Graph Cut Theory

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IEEE Access 2025年 13卷 76868-76878页

作者： Yao, Guilin Liu, Heyuan Zhang, Dongliang School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China

image segmentation, a fundamental problem in image processing, involves distinguishing the foreground from the background. Traditional image segmentation methods are typically divided into local and global approaches. Local methods often result in blurred segmentation due to their reliance on overly localized color sampling models. Conversely, global methods, which depend on the overall color distribution, struggle with images where the foreground and background share similar global characteristics. To overcome these limitations, a novel image segmentation method that integrates nonlocal criteria with graph cut theory is proposed. Initially, the method transforms the image's color space to the HSV color space, enhancing color contrast. Following this, nonlocal criteria are utilized to compute the data term within the graph cut model, which is then incorporated into the traditional graph cut framework for final segmentation. This approach broadens the neighborhood sampling range in local models, allowing the model to identify more valuable nonlocal samples while mitigating the impact of color similarity in global models. Experimental results across various datasets indicate a marked improvement in segmentation quality achieved by the proposed method. © 2013 IEEE.

关键词： image segmentation

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image segmentation Network Based on Convolutional Layers, a Clustering Layer and Cluster Classification 37th

Image Segmentation Network Based on Convolutional Layers, a ...

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37th International Conference on Computer Applications in Industry and Engineering

作者： Rosas-Romero, Roberto Univ Amer Puebla San Andres Cholula Puebl Mexico

ISBN: (纸本)9783031762727;9783031762734

This work contributes to the detection of regions of interest on images and their corresponding classification in medical imaging applications by introducing an image segmentation network that consists of four stages. In the first stage, multi-resolution processing is applied to outline regions where further segmentation and classification are to be conducted. Subsequently, a quad-tree division stage followed by a clustering stage deliver an image that is divided into unlabeled clusters. The output stage assigns each cluster to one class. This architecture offers flexibility in the input and output stages since this network can be fed with images of any size and the output stage can be implemented with any traditional classification model such as k-nearest neighbors, multi-layer perceptron, and support vector machine. Another contribution of this work is that this network does not rely on a very large number of annotated images for its training.

关键词： image segmentation Convolutional Neural Networks Multi-Resolution Analysis Clustering

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image segmentation

Image Segmentation

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作者： Mikeš, Stanislav Charles University of Prague

image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...

关键词： image segmentation texture benchmark

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Delicate image segmentation based on cosine kernel graph cut

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2025年 108卷

作者： Niazi, Mehrnaz Rahbar, Kambiz Taheri, Fatemeh Sheikhan, Mansour Khademi, Maryam Pishtazan Higher Educ Inst Dept Comp Engn Shiraz Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Appl Math South Tehran Branch Tehran Iran

The kernel graph cut approach is effective but highly dependent on the choice of kernel used to map data into new feature space. This study introduces an enhanced kernel-based graph cut method specifically designed for segmenting complex images. The proposed method extends the RBF kernel by incorporating a unique mapping function that includes two components from the MacLaurin cosine kernel series, known for its ability decorrelate regions and compress energy. This enhanced feature space enables the objective function to include data fidelity term, which preserves the standard deviation of each region's data in the segmented image, along with a regularization term that maintains smooth boundaries. The proposed method retains the computational efficiency typical of graph-based techniques while enhancing segmentation accuracy for intricate images. Experimental evaluations on widely-used datasets with complex shapes and fine boundaries demonstrate the effectiveness of this kernel-based approach compared to existing methods.

关键词： image segmentation Graph cut Delicate images MacLaurin cosine kernel

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FlowSDF: Flow Matching for Medical image segmentation Using Distance Transforms

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年 1-13页

作者： Bogensperger, Lea Narnhofer, Dominik Falk, Alexander Schindler, Konrad Pock, Thomas Graz Univ Technol Inst Comp G & Vis Graz Austria Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland

Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effective in modeling the statistical properties of segmentation masks that are closely related to the respective structures. In this work we introduce FlowSDF, an image-guided conditional flow matching framework, designed to represent the signed distance function (SDF), and, in turn, to represent an implicit distribution of segmentation masks. The advantage of leveraging the SDF is a more natural distortion when compared to that of binary masks. Through the learning of a vector field associated with the probability path of conditional SDF distributions, our framework enables accurate sampling of segmentation masks and the computation of relevant statistical measures. This probabilistic approach also facilitates the generation of uncertainty maps represented by the variance, thereby supporting enhanced robustness in prediction and further analysis. We qualitatively and quantitatively illustrate competitive performance of the proposed method on a public nuclei and gland segmentation data set, highlighting its utility in medical image segmentation applications.

关键词： Flow matching image segmentation Conditional generative models Signed distance function

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Ultra-Lightweight Network for Medical image segmentation Inspired by Bio-Visual Interaction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第4期35卷 3486-3497页

作者： Cai, Zhefei Fan, Yingle Zhu, Minwei Fang, Tao Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Univ Miami Dept Biomed Engn Miami FL 33124 USA

Computer-aided medical image segmentation helps to assist physicians in locating lesion area for the subsequent diagnosis and treatment. Due to the irregular shape of the target and the uneven sample size between the target and the background area, automatic segmentation of medical images is a challenging task. Many CNN-Based, Transformer-Based models deepen the number of network layers or introduce complex modules in order to improve the segmentation accuracy. Limited by the computational resources, these types of large models are not suitable for the actual clinical environment. Inspired by the rapidity, accuracy, and low consumption characteristics of bio-visual processing, the Ultra-Lightweight Network Inspired by Bio-Visual Interaction (BVI-Net) is constructed in this paper. The Global Pathway is constructed by simulating the dorsal stream, in order to extract global features rapidly, and the Local Pathway is constructed by simulating the ventral stream, in order to process local features finely. At the same time, the skip connection module integrating Graph Convolutional Network (GCN) attention mechanism is constructed to simulate the synchronous integration ability of the visual pathway for multi-level features. The International Skin Imaging Collaboration (ISIC) dataset, the Liver Tumor segmentation (LiTS) dataset, and the Brain Tumor segmentation Challenge (BraTS) dataset are used for experiments. The BVI-Net proposed in this paper requires only 0.026M parameters to achieve the excellent performance in three representative medical image segmentation datasets, which has certain advantages over state-of-the-art (SOTA) methods. The biological vision mechanism and the artificial intelligence algorithm are integrated in this paper, which provides new ideas for the construction of biological vision-guided deep learning models and promotes the development of biomimetic computational vision.

关键词： Feature extraction image segmentation Transformers Kernel Accuracy Visualization Computational modeling Biological system modeling Lesions Streaming media Biological visual pathway dual encoder architecture GCN attention mechanism state space models medical image segmentation

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Exploring Contrastive Pre-Training for Domain Connections in Medical image segmentation

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2025年第4期44卷 1686-1698页

作者： Zhang, Zequn Jiang, Yun Wang, Yunnan Xie, Baao Zhang, Wenyao Li, Yuhang Chen, Zhen Jin, Xin Zeng, Wenjun Univ Sci & Technol China USTC Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China Eastern Inst Technol EIT Ningbo Ningbo 315200 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730071 Peoples R China EIT Ningbo Ningbo Inst Digital Twin Ningbo 315200 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Hong Kong Peoples R China

Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the generalization of deep models by alleviating domain gaps caused by inconsistency across equipment, imaging protocols, and patient conditions. However, existing UDA works remain insufficiently explored and present great limitations: 1) Exhibit cumbersome designs that prioritize aligning statistical metrics and distributions, which limits the model's flexibility and generalization while also overlooking the potential knowledge embedded in unlabeled data;2) More applicable in a certain domain, lack the generalization capability to handle diverse shifts encountered in clinical scenarios. To overcome these limitations, we introduce MedCon, a unified framework that leverages general unsupervised contrastive pre-training to establish domain connections, effectively handling diverse domain shifts without tailored adjustments. Specifically, it initially explores a general contrastive pre-training to establish domain connections by leveraging the rich prior knowledge from unlabeled images. Thereafter, the pre-trained backbone is fine-tuned using source-based images to ultimately identify per-pixel semantic categories. To capture both intra- and inter-domain connections of anatomical structures, we construct positive-negative pairs from a hybrid aspect of both local and global scales. In this regard, a shared-weight encoder-decoder is employed to generate pixel-level representations, which are then mapped into hyper-spherical space using a non-learnable projection head to facilitate positive pair matching. Comprehensive experiments on diverse medical image datasets confirm that MedCon outperforms previous methods by effectively managing a wide range of domain shifts and showcasing superior generalization capabilities.

关键词： Training image segmentation Generators Measurement Data models Anatomical structure Semantics Prototypes Head Buildings Domain connectivity unsupervised domain adaptation unsupervised contrastive pre-training cross-domain medical image segmentation

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DRLSU-Net: Level set with U-Net for medical image segmentation

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DIGITAL SIGNAL PROCESSING 2025年 157卷

作者： Wang, Xiaofeng Liu, Jiashan Yang, Rentao Wu, Zhize Sun, Lingma Zou, Le Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Anhui Peoples R China

Convolutional neural networks (CNN) have been extensively utilized for image segmentation tasks, with the UNet architecture emerging as a classical model in medical imaging due to its simple structure and high scalability. However, for complex medical images, particularly those with blurred lesion boundaries, the U-Net model often loses significant edge information during feature extraction. Each layer in the encoder section is convolved with a simple stack of equal design, which is clearly not able to obtain sufficient feature information from an otherwise low-quality image. In order to solve this problem, the DRLSU-Net model is proposed which combines an enhanced U-Net architecture with distance regularized level set evolution (DRLSE). The DRLSU-Net model takes the results of U-Net pre-segmentation as an intermediate medium, combines the U-Net model with the level set method. Indirectly represent the target contour through the zero level set to obtain a more intuitive target edge location. Specifically, the Parallel Dilated Convolutional Sequence (PDCS) is introduced in the U-Net encoder to minimize information loss during down-sampling, and preserve more edge details. Secondly, the Mixed Attention Mechanism (MAM) is introduced into the decoder, aiding the network in recovering important information during image reconstruction, thus generating a more accurate output sequence. Finally, the pre-segmentation label mapping is converted into a level set function representation, which serves as a priori information for the level set method. A new energy functional is constructed to guide the evolution of the level set curves, helping to obtain a clear contour boundary. The performance of the DRLSU-Net model is evaluated on the ISIC2017, ISIC2018, CVC-ClinicDB, and Lung datasets. Extensive experiment results show better performance than other state-of-the-art (SOTA) methods in terms of mIoU and F1-socre, and the results indicate that the DRLSU-Net model performs competit

关键词： Convolutional neural network image segmentation U-Net Level set

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