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patch-based segmentation using refined multifeature for magnetic resonance prostate images

OPTIK 2016年第2期127卷 732-737页

作者： Huang, Zhe Jiang, Shan Ding, Yabin Cai, Jing Liu, Jun Yang, Jun Yang, Zhiyong Tianjin Univ Sch Mech Engn Ctr Adv Mech & Robot 92 Weijin Rd Tianjin 300072 Peoples R China Tianjin Univ Technol Sch Comp & Commun Engn Tianjin 300384 Peoples R China Tianjin Union Med Ctr Dept Imaging 190 Jieyuan Rd Tianjin 300121 Peoples R China

In this paper, a new method is proposed for prostate segmentation in magnetic resonance (MR) images, which is an important step for image-guided brachytherapy system. Different from the conventional patch-based segmentation method, a multicharacter feature set is proposed within the range of statistical feature on voxel intensity, texture feature description, shape features, and spatial relation feature, to resolve the limitations of incomprehensive feature set. Furthermore, an Adaptive Boosting algorithm is used to refine these features in order to reduce the redundancy of features. Using the refined features, the patch-based segmentation framework has a better effect. The method is tested by 40 prostate MR images and compared with several classical image segmentation methods;the test and comparison results show that the proposed method consistently achieves higher segmentation accuracy. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Image-guided brachytherapy system Prostate MR image feature patch-based segmentation

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patch-based segmentation OF OVERLAPPING CERVICAL CELLS USING ACTIVE CONTOUR WITH LOCAL EDGE INFORMATION

PATCH-BASED SEGMENTATION OF OVERLAPPING CERVICAL CELLS USING...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Khadidos, Alaa Sanchez, Victor Li, Chang-Tsun Univ Warwick Dept Comp Sci Warwick England

ISBN: (纸本)9781509041176

The Pap test is a manual screening procedure that is used to detect the precursor lesions of cervical cancer by analyzing changes in nuclei and cytoplasms of cervical cells. Due to the sensitivity of the Pap test to intra-and inter-observer variability, automating the procedure using digital image analysis test is still an open problem. Within this context, segmentation of overlapping cervical cells is a key component to develop image analysis methods. In this paper, we propose a framework for segmenting the cytoplasm of each individual cell depicted within an image of overlapping cervical cells. The proposed framework uses a patch-based approach where a parametric active contour detects, on a patch-by-path basis, the cytoplasm boundary of each overlapping cell. The active contour within the patch deforms under the influence of Gradient Vector Flow (GVF) forces computed based on the local edges depicted in each patch region. Results show that the proposed framework achieves more accurate cytoplasm segmentation results compared to the current state-of-art methods.

关键词： Active contours overlapping cervical cells Pap test patch-based segmentation Gradient Vector Flow (GVF)

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patch Forest: A Hybrid Framework of Random Forest and patch-based segmentation

Patch Forest: A Hybrid Framework of Random Forest and Patch-...

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Conference on Medical Imaging - Image Processing

作者： Xie, Zhongliu Gillies, Duncan Imperial Coll London Dept Comp London England

ISBN: (纸本)9781510600195

The development of an accurate, robust and fast segmentation algorithm has long been a research focus in medical computer vision. State-of-the-art practices often involve non-rigidly registering a target image with a set of training atlases for label propagation over the target space to perform segmentation, a.k.a. multi-atlas label propagation (MALP). In recent years, the patch-based segmentation (PBS) framework has gained wide attention due to its advantage of relaxing the strict voxel-to-voxel correspondence to a series of pair-wise patch comparisons for contextual pattern matching. Despite a high accuracy reported in many scenarios, computational efficiency has consistently been a major obstacle for both approaches. Inspired by recent work on random forest, in this paper we propose a patch forest approach, which by equipping the conventional PBS with a fast patch search engine, is able to boost segmentation speed significantly while retaining an equal level of accuracy. In addition, a fast forest training mechanism is also proposed, with the use of a dynamic grid framework to efficiently approximate data compactness computation and a 3D integral image technique for fast box feature retrieval.

关键词： patch-based segmentation random forest hippocampus segmentation

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A patch-based segmentation Approach with High Level Representation of the Data for Cortical Sulci Recognition 4th

A Patch-Based Segmentation Approach with High Level Represen...

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4th International Workshop on patch-based Techniques in Medical Imaging (patch-MI)

作者： Borne, Leonie Mangin, Jean-Francois Riviere, Denis CEA Saclay Neurospin F-91191 Gif Sur Yvette France

ISBN: (纸本)9783030005009;9783030004996

Because of the strong variability of the cortical sulci, their automatic recognition is still a challenging problem. The last algorithm developed in our laboratory for 125 sulci reaches an average recognition rate around 86%. It has been applied to thousands of brains for morphometric studies (***). A weak point of this approach is the modeling of the training dataset as a single template of sulcus-wise probability maps, losing information about the alternative patterns of each sulcus. To overcome this limit, we propose a different strategy inspired by Multi-Atlas segmentation (MAS) and more particularly the patch-based approaches. As the standard way of extracting patches does not seem capable of exploiting the sulci geometry and the relations between them, which we believe to be the discriminative features for recognition, we propose a new patch generation strategy based on a high level representation of the sulci. We show that our new approach is slightly, but significantly, better than the reference one, while we still have an avenue of potential refinements that were beyond reach for a single template strategy.

关键词： MRI Cortical sulci labeling patch-based segmentation

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patch-based segmentation of overlapping cervical cells using active contour with local edge information

Patch-based segmentation of overlapping cervical cells using...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Alaa Khadidos Victor Sanchez Chang-Tsun Li Department of Computer Science University of Warwick UK

ISBN: (纸本)9781509041183

The Pap test is a manual screening procedure that is used to detect the precursor lesions of cervical cancer by analyzing changes in nuclei and cytoplasms of cervical cells. Due to the sensitivity of the Pap test to intra- and inter-observer variability, automating the procedure using digital image analysis test is still an open problem. Within this context, segmentation of overlapping cervical cells is a key component to develop image analysis methods. In this paper, we propose a framework for segmenting the cytoplasm of each individual cell depicted within an image of overlapping cervical cells. The proposed framework uses a patch-based approach where a parametric active contour detects, on a patch-by-path basis, the cytoplasm boundary of each overlapping cell. The active contour within the patch deforms under the influence of Gradient Vector Flow (GVF) forces computed based on the local edges depicted in each patch region. Results show that the proposed framework achieves more accurate cytoplasm segmentation results compared to the current state-of-art methods.

关键词： Active contours overlapping cervical cells Pap test patch-based segmentation Gradient Vector Flow (GVF) Papanicolaou test Active contours Cytoplasm contour segmentation segmentation Interobserver Variation Cells overlaps

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An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2018年 70卷 73-82页

作者： Tor-Diez, Carlos Passat, Nicolas Bloch, Isabelle Faisan, Sylvain Bednarek, Nathalie Rousseau, Francois UBL IMT Atlantique LaTIM U1101 INSERM Brest France Univ Reims CReSTIC Reims France Univ Paris Saclay Telecom ParisTech LTCI Paris France Univ Strasbourg CNRS FMTS ICube UMR 7357 Strasbourg France CHU Reims Serv Med Neonatale & Reanimat Pediat Reims France

Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches;second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-atlas patch-based segmentation Iterative segmentation Neonatal MRI Cortex

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An improved label fusion approach with sparse patch-based representation for MRI brain image segmentation

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2017年第1期27卷 23-32页

作者： Yan, Meng Liu, Hong Xu, Xiangyang Song, Enmin Qian, Yuejing Pan, Ning Jin, Renchao Jin, Lianghai Cheng, Shaorong Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Hubei Key Lab Med Informat Anal & Tumor Diag Trea Wuhan 430074 Hubei Peoples R China Zhejiang Ind & Trade Vocat Coll Wenzhou 325000 Zhejiang Peoples R China South Cent Univ Natl Coll Biomed Engn Wuhan 430074 Hubei Peoples R China Wuhan Gen Hosp Guangzhou Mil Dept Radiol Wuhan 430074 Hubei Peoples R China Kennesaw State Univ Ctr Machine Vision & Secur Res Georgia GA 30144 USA

The multi-atlas patch-based label fusion (LF) method mainly focuses on the measurement of the patch similarity which is the comparison between the atlas patch and the target patch. To enhance the LF performance, the distribution probability about the target can be used during the LF process. Hence, we consider two LF schemes: in the first scheme, we keep the results of the interpolation so that we can obtain the labels of the atlas with discrete values (between 0 and 1) instead of binary values in the label propagation. In doing so, each atlas can be treated as a probability atlas. Second, we introduce the distribution probability of the tissue (to be segmented) in the sparse patch-based LF process. based on the probability of the tissue and sparse patch-based representation, we propose three different LF methods which are called LF-Method-1, LF-Method-2, and LF-Method-3. In addition, an automated estimation method about the distribution probability of the tissue is also proposed. To evaluate the accuracy of our proposed LF methods, the methods were compared with those of the nonlocal patch-based LF method (Nonlocal-PBM), the sparse patch-based LF method (Sparse-PBM), majority voting method, similarity and truth estimation for propagated segmentations, and hierarchical multi-atlas LF with multi-scale feature representation and label-specific patch partition (HMAS). based on our experimental results and quantitative comparison, our methods are promising in the magnetic resonance image segmentation. (C) 2017 Wiley Periodicals, Inc.

关键词： brain image segmentation sparse representation patch-based segmentation distribution probability label fusion

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A Probabilistic patch-based Label Fusion Model for Multi-Atlas segmentation With Registration Refinement: Application to Cardiac MR Images

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2013年第7期32卷 1302-1315页

作者： Bai, Wenjia Shi, Wenzhe O'Regan, Declan P. Tong, Tong Wang, Haiyan Jamil-Copley, Shahnaz Peters, Nicholas S. Rueckert, Daniel Univ London Imperial Coll Sci Technol & Med Biomed Image Anal Grp Dept Comp London SW7 2RH England Univ London Imperial Coll Sci Technol & Med Hammersmith Hosp MRC Clin Sci Ctr Robert Steiner MRI Unit London W12 0NN England Imperial Coll Healthcare NHS Trust St Marys Hosp Dept Cardiol London W2 1PG England

The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis.

关键词： Image registration image segmentation multi-atlas segmentation patch-based segmentation

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A Fuzzy Rule-based System using a patch-based Approach for Semantic segmentation in Floor Plans

A Fuzzy Rule-based System using a Patch-based Approach for S...

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IEEE CIS International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Leon-Garza, Hugo Hagras, Hani Pena-Rios, Anasol Conway, Anthony Owusu, Gilbert Univ Essex Computat Intelligence Ctr Sch Comp Sci & Elect Engn Colchester Essex England BT Plc BT Labs Ipswich Suffolk England

ISBN: (纸本)9781665444071

Semantic segmentation models help with the extraction of information from images. Currently, Convolutional Neural Networks (CNNs) are the state of the art for performing such tasks but the interpretability in their predictions is low. Previous work had proposed the use of Fuzzy Logic Rule-based systems (FRBS) as an explainable AI classifier of pixels for segmentation of images. In this paper, we extend that approach by using the similarity between image patches as context information for our model. The type-1 FRBS that uses the proposed set of context information features reaches an average Intersection over Union (IoU) value 3.51% higher than the type-1 FRBS using colour information. The difference in average IoU is significant due to the importance of colour in the testing images and the already high IoU value from the type-1 FRBS using colour.

关键词： semantic segmentation floor plans patch-based segmentation fuzzy logic rule-based systems

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patch-based partial motion blurred segmentation

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International Journal of Cognitive Computing in Engineering 2020年 1卷 45-54页

作者： Xiang, Lin Jiang, Xiaoling Xu, Yueqin Zhang, Yongjun Zhu, Tongqing Huaiyin Institute of Technology Huai'an 223003 Jiangsu China

Motion blur has a significant impact on image recognition. segmentation of motion-blurred regions contributes to further identification or classification. Most existed segmentation algorithms are always universal for partially blur images, but not especially for motion-blurred ones. This paper proposes a particular algorithm aiming at motion-blurred region segmentation. Firstly, motion regions are segmented by a patch-based preprocessing. Then, the blurriness of motion regions is measured by a defined function to detect local blurred areas. Empirical thresholds are recommended according to the experimental results. The experimental results show that the motion-blurred regions can be segmented more accurately, and the speed almost doubles other algorithms. Thus we propose a more accurate and efficient segmentation method, especially for partial motion-blurred images. © 2020

关键词： Image segmentation Local motion-blurred patch-based segmentation

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