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arXiv 2018年

作者： Huang, Yi-Jie Dou, Qi Wang, Zi-Xian Liu, Li-Zhi Jin, Ying Li, Chao-Feng Wang, Lisheng Chen, Hao Xu, Rui-Hua Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Imsight Medical Technology Co. Ltd. China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localization-segmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detail-preserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available. Copyright ©

关键词： Signal encoding

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Dictionary learning based statistical interior reconstruction without a prior knowledge 10

Dictionary learning based statistical interior reconstructio...

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Developments in X-Ray Tomography X

作者： Shi, Yongyi Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi710049 China

ISBN: (纸本)9781510603257

Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint. © Copyright 2016 SPIE.

关键词： image segmentation

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Shape classification according to LBP persistence of critical points 19th

Shape classification according to LBP persistence of critica...

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19th IAPR International Conference on Discrete Geometry for Computer imagery, DGCI 2016

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms TU Wien Vienna Austria

ISBN: (纸本)9783319323596

This paper introduces a shape descriptor based on a combination of topological image analysis and texture information. Critical points of a shape’s skeleton are determined first. The shape is described according to persistence of the local topology at these critical points over a range of scales. The local topology over scale-space is derived using the local binary pattern texture operator with varying radii. To visualise the descriptor, a new type of persistence graph is defined which captures the evolution, respectively persistence, of the local topology. The presented shape descriptor may be used in shape classification or the grouping of shapes into equivalence classes. Classification experiments were conducted for a binary image dataset and the promising results are presented. Because of the use of persistence, the influence of noise or irregular shape boundaries (e.g. due to segmentation artefacts) on the result of such a classification or grouping is bounded. © Springer International Publishing Switzerland 2016.

关键词： Local binary pattern

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Scale-space anisotropic total variation for limited angle tomography

arXiv

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arXiv 2017年

作者： Huang, Yixing Taubmann, Oliver Huang, Xiaolin Haase, Viktor Lauritsch, Guenter Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Erlangen Germany Pattern Recognition Lab FriedrichAlexander-University Erlangen-Nuremberg Erlangen Germany Erlangen Germany Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Siemens Healthcare GmbH Forchheim Germany Department of Radiology University of Utah Salt Lake CityUT United States

This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2 · s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales. Copyright © 2017, The Authors. All rights reserved.

关键词： Anisotropy

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Fast KNN search for big data with set compression tree and best bin first 2

Fast KNN search for big data with set compression tree and b...

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2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016

作者： Chen, Zhenjie Yan, Jingqi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of Ministry of Education for System Control and Information Processing China Shanghai China

ISBN: (纸本)9781467398213

This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily. © 2016 IEEE.

关键词： Big data

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Multi-stage suture detection for robot assisted anastomosis based on deep learning

arXiv

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arXiv 2017年

作者： Hu, Yang Gu, Yun Yang, Jie Yang, Guang-Zhong Hamlyn Centre for Robotic Surgery Imperial College London SW72AZ United Kingdom School of Biomedical Engineering Institute of Image Processing & Pattern Recognition Shanghai Jiao Tong University Shanghai China

In robotic surgery, task automation and learning from demonstration combined with human supervision is an emerging trend for many new surgical robot platforms. One such task is automated anastomosis, which requires bimanual needle handling and suture detection. Due to the complexity of the surgical environment and varying patient anatomies, reliable suture detection is difficult, which is further complicated by occlusion and thread topologies. In this paper, we propose a multi-stage framework for suture thread detection based on deep learning. Fully convolutional neural networks are used to obtain the initial detection and the overlapping status of suture thread, which are later fused with the original image to learn a gradient road map of the thread. Based on the gradient road map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two different types of sutures demonstrate the accuracy of the proposed framework. Copyright © 2017, The Authors. All rights reserved.

关键词： Robotic surgery

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Cerebral ASL, SPECT and MRI image registration, enhancing fusion and joint analysis system

Cerebral ASL, SPECT and MRI image registration, enhancing fu...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Ye Liu Yang Wang Lisheng Wang Shaoli Song Institute of Image Processing and Pattern Recognition Department of AutomationShanghai Jiao Tong University Department of Nuclear Medicine Ren Ji HospitalSchool of MedicineShanghai Jiao tong University

ISBN: (纸本)9781509001668

There is no technique to do a joint analysis of Arterial Spin Labeling(ASL),Single Photon Emission Computed Tomography(SPECT) and Magnetic Resonance Imaging(MRI) *** this paper,we propose a method to realize multimodality image registration,enhancing fusion and features calculation of 3D volume of *** mutual information registration,VTK image fusion,pseudo-color mapping,spline interpolation,3D tomographic interpolation,region growth and other algorithms,we build a joint analysis *** test results show that this system provides accurate registration function and more distinguishable images for the ***,gray average of pathological brain area is calculated as regional cerebral blood flow(r CBF),which provides the basis for clinicians to make confirmed diagnoses.

关键词： ASL SPECT MRI Registration Enhancing fusion Volume of interest Cerebral blood flow

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Nonconvex penalties with analytical solutions for one-bit compressive sensing

arXiv

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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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CT image Based 3D Orbital Prosthesis Model Construction Method

CT Image Based 3D Orbital Prosthesis Model Construction Meth...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Chunyuan Xu Shuang Li Lisheng Wang Caiwen Xiao Institute of Image Processing and Pattern Recognition Department of AutomationShanghai Jiao Tong University Department of Ophthalmology Ninth People's HospitalSchool of MedicineShanghai Jiao Tong University

ISBN: (纸本)9781509001668

To solve the clinical problem of constructing an aesthetic and appropriate orbital prosthesis model,two methods based on volume cutting and surface cutting respectively are proposed in this *** spline interpolation,region growth,triangular patch calculation,cylinder cutting and other techniques,we obtain the 3D orbital prosthesis model with a proper size and smooth ***,a visual feedback mechanism is added in the process of prosthesis construction to make the prosthesis model fit the defect part *** results show that the CT image based 3D orbital prosthesis model construction method in this paper significantly improves the clinical acceptability and effectiveness.

关键词： orbital prosthesis volume cutting surface cutting visual feedback mechanism

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Supervised recurrent hashing for large scale video retrieval 16

Supervised recurrent hashing for large scale video retrieval

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24th ACM Multimedia Conference, MM 2016

作者： Gu, Yun Ma, Chao Yang, Jie School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781450336031

Hashing for large-scale multimedia is a popular research topic, attracting much attention in computer vision and visual information retrieval. Previous works mostly focus on hashing the images and texts while the approaches designed for videos are limited. In this paper, we propose a Supervised Recurrent Hashing (SRH) that explores the discriminative representation obtained by deep neural networks to design hashing approaches. The long-short term memory (LSTM) network is deployed to model the structure of video samples. The max-pooling mechanism is introduced to embedding the frames into fixed-length representations that are fed into supervised hashing loss. Experiments on UCF-101 dataset demonstrate that the proposed method can significantly outperforms several state-of-the-art methods. © 2016 ACM.

关键词： Long short-term memory

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