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Robust Segmentation of Intima-Media Borders With Different Morphologies and Dynamics During the Cardiac Cycle

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2018年第5期22卷 1571-1582页

作者： Zhao, Shen Gao, Zhifan Zhang, Heye Xie, Yaoqin Luo, Jianwen Ghista, Dhanjoo Wei, Zhanghong Bi, Xiaojun Xiong, Huahua Xu, Chenchu Li, Shuo Chinese Acad Sci Shenzhen Inst Adv Technol Inst Biomed & Hlth Engn Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Inst Adv Comp & Digital Engn Shenzhen 518055 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China Univ 2020 Fdn Northborough MA USA Shenzhen Peoples Hosp Shenzhen Peoples R China Tongji Hosp Wuhan 430030 Hubei Peoples R China Second Hosp Shenzhen Shenzhen 518055 Peoples R China Anhui Univ Hefei Anhui Peoples R China Univ Western Ontario London ON N6A 3K7 Canada

Segmentation of carotid intima-media (IM) borders from ultrasound sequences is challenging because of unknown image noise and varying IM border morphologies and/or dynamics. In this paper, we have developed a state-space framework to sequentially segment the carotid IM borders in each image throughout the cardiac cycle. In this framework, an H-infinity filter is used to solve the state-space equations, and a grayscale-derivative constraint snake is used to provide accurate measurements for the H-infinity filter. We have evaluated the performance of our approach by comparing our segmentation results to the manually traced contours of ultrasound image sequences of three synthetic models and 156 real subjects from four medical centers. The results show that our method has a small segmentation error (lumen intima, LI: 53 +/- 67 mu m;media-adventitia, MA: 57 +/- 63 mu m) for synthetic and real sequences of different image characteristics, and also agrees well with the manual segmentation (LI: bias = 1.44 mu m;MA: bias = -3.38 mu m). Our approach can robustly segment the carotid ultrasound sequences with various IM border morphologies, dynamics, and unknown image noise. These results indicate the potential of our framework to segment IM borders for clinical diagnosis.

关键词： Carotid artery H-infinity filter IM borders snake algorithm ultrasound image sequences

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An approach for prediction of shoreline with spatial uncertainty mapping (SLiP-SUM)

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2018年 73卷 546-554页

作者： San, Bekir Taner Ulusar, Umit Deniz Akdeniz Univ Dept Geol Engn Dumlupinar Blv TR-07058 Antalya Turkey Akdeniz Univ Dept Comp Engn Dumlupinar Blv TR-07058 Antalya Turkey

This study puts forward a semi-automatic shoreline detection and future prediction with spatial uncertainty algorithm called the SUP-SUM (Shore Line Prediction with Spatial Uncertainty Mapping), which has five main steps: (1) preprocessing of data sets (i.e. aerial photos and/or satellite images), (2) extraction or delineation of the existing shorelines with snake algorithm, 3) prediction of the future shorelines using linear regression, (4) preparation of the spatial uncertainty mapping using cokriging, and (5) producing possible shoreline with spatial uncertainty. The proposed approach was tested on the coast of Kumluca, a dynamic coastal area in Turkey, and future shoreline predictions were made for 2020, 2025, 2030 and 2035 based on remotely sensed data (aerial photos and WorldView-2 images) acquired between 1971 and 2014. A cokriging interpolation technique was adopted that takes into account both estimation errors and the spatial resolution of the source data sets for the mapping of spatial uncertainty. The results indicate that the maximum and minimum annual changes of shoreline were -2.29 m/yr (transgression) in the west-central part of the study area, and + 0.36 m/yr (regression) in the east part. The general trend of the future shoreline regime was transgressional, and the mean uncertainty values along the predicted shorelines for 2020, 2025, 2030 and 2035 were 6.78 m, 2.02 m, 6.76 m and 7.06 m, respectively.

关键词： Shoreline prediction Spatial uncertainty mapping snake algorithm SLiP SUM Antalya

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A Wheel-based Side-view Car Detection using snake algorithm

A Wheel-based Side-view Car Detection using Snake Algorithm

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IEEE 6th International Conference on Information and Automation for Sustainability (ICIAFS)

作者： Vinoharan, Veerapathirapillai Ramanan, Amirthalingam Kodituwakku, Saluka R. Univ Jaffna Comp Unit Jaffna Sri Lanka Univ Jaffna Dept Comp Sci Jaffna Sri Lanka Univ Peradeniya Dept Comp Sci & Stat Peradeniya Sri Lanka

ISBN: (纸本)9781467319751;9781467319768

Car detection is a challenging research area where the appearance has various changes due to different models, poses, scales, lighting conditions, background, occlusion and various image sizes. Amidst these external challenging factors, car side-views have obvious and consistent characteristics in their structure such as wheels, oblique windows and bumpers, which provide crucial cues for detection. In this paper, we describe a novel side-view car detection technique which constructs an initial contour for the snake using the detection of wheels. Our approach is subdivided in to three stages: wheel detection, bounding box detection, and segmenting the car from its background. In the first step, we use Hough transformation to detect circles in an image and validate the wheels with a learnt visual vocabulary. In the second step, we construct an initial contour with the aid of the detected wheels and the width-to-height statistics of different types of cars. This initial contour forms the bounding box of the car. In the third step, the snake algorithm proceeds with the initial contour to fit the boundary closer to the car. Hence, the foreground, car, is separated from the background. We empirically evaluated our method on 100 side-view cars and the testing results shows that this approach has good performance.

关键词： Car detection Circular Hough Transform snake algorithm SURF Visual Vocabulary

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Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年 105卷 193-211页

作者： Saeedmanesh, Mohammadreza Geroliminis, Nikolas Ecole Polytech Fed Lausanne Sch Architecture Civil & Environm Engn Urban Transport Syst Lab GC C2 390Stn 18 CH-1015 Lausanne Switzerland

The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be studied in the spatiotemporal dimension. Investigating the clustering problem over time in the dynamic domain is critical to better understand and reveal the hidden information during the process of congestion formation and dissolution. The primary motivation of the paper is to study the spatiotemporal relation of congested links, observing congestion propagation from a macroscopic perspective, and finally identifying critical pockets of congestion that can aid the design of peripheral control strategies. To achieve this, we first introduce a static clustering method to partition the heterogeneous network into homogeneous connected sub-regions. The proposed framework guarantees connectivity of the cluster in different steps, which eases the development of a dynamic framework. The proposed clustering approach has 3 steps;firstly, it obtains a set of homogeneous connected components in the network. Each component has a form of sequence which is built by sequentially adding neighboring links with similar level of congestion. Secondly, the major skeleton of clusters is obtained out of this feasible set by minimizing a heterogeneity index. Thirdly, a fine-tuning step is designed to complete the clustering task by assigning the unclustered links of the network to proper clusters while keeping the connectivity. The optimization problem in both second and third step is formulated as a mixed integer linear programming. The approach is also extended to capture spatiotemporal growth and formation of congestion. The dynamic clustering is based on an iterative and fast procedure that considers the spatiotemporal characteristics of congestion propagation and identifies the links with the highest degree of heterogeneity due to t

关键词： Graph partitioning Congestion propagation Asggregate modeling Mixed integer linear programming snake algorithm

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Automatic algorithm for Segmentation of Atherosclerotic Carotid Plaque

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JOURNAL OF STROKE & CEREBROVASCULAR DISEASES 2017年第2期26卷 411-416页

作者： Bonanno, Lilla Sottile, Fabrizio Ciurleo, Rosella Di Lorenzo, Giuseppe Bruschetta, Daniele Bramanti, Alessia Ascenti, Giorgio Bramanti, Placido Marino, Silvia IRCCS Ctr Neurolesi Bonino Pulejo SS 113Via Palermo I-98124 Messina Italy Univ Messina Dept Clin & Expt Med Messina Italy Univ Messina Dept Biomed & Dent Sci & Morphofunct Imaging Messina Italy

Background: Carotid atherosclerosis is one of the major causes of stroke. The determination of the intima-media thickness, the identification of carotid atherosclerotic plaque, and the classification of the different stenoses are considered as important parameters for the assessment of atherosclerotic diseases. The aim of this work is to segment the plaques and to allow a better comprehension of carotid atherosclerosis. Methods: We considered 44 subjects, 22 with and 22 without the presence of plaques in the carotid axis, and we applied the snake algorithm. Results: The resulting interclass correlation coefficients (ICCs) were significant for all 3 parameters (mean echogenicity: ICC1 = .78 [95% CI: .55-0.90];perimeter: ICC2 = .81 [95% CI: .61-0.92];area: ICC3 = .89 [95% CI: .75-0.95]). The diagnostic accuracy was 82%, with an appropriate cutoff value of 224.5, sensitivity of 79%, and specificity of 85%. Conclusions: In this study, we developed an automatic method to identify the carotid plaque. Our results showed that an automatic system of image segmentation could be used to identify, characterize, and measure atherosclerotic carotid plaques.

关键词： Automatic segmentation carotid atherosclerosis snake algorithm ultrasound image

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Skin-Colored Gesture Recognition and Support Vector Machines-Based Classification of Light Sources by Their Illumination Properties 3rd

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3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Bandyopadhyay, Shreyasi Choudhury, Sabarna Ghosh, Riya Santra, Saptam Ghosh, Rabindranath St Thomas Coll Engn & Technol Elect & Commun Dept Kolkata India

ISBN: (纸本)9789811038747;9789811038730

The illumination characteristics of light sources can determine whether the light source is a normal or a faulty one. The proposed work is based on moving a platform containing a light source in both horizontal and vertical directions by gesture recognition. The gesture recognition done by Fuzzy C means and snake algorithm-based skin color detection makes the recognition more accurate. The illumination values of the light source are obtained by a webcam. The set of data helps in classification of the state of an unknown light source (normal or faulty) by support vector machines with radial basis function as kernel with a yield of an error rate of about 0.6% marking the efficacy of the system and making the system a novel and sophisticated one.

关键词： Illumination characteristics Gesture recognition Skin color detection Fuzzy C means snake algorithm Support vector machines Radial basis function

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Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

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Transportation Research Procedia 2017年 23卷 962-979页

作者： Mohammadreza Saeedmanesh Nikolas Geroliminis Ecole Polytechnique Federale de Lausanne (EPFL) School of Architecture Civil and Environmental Engineering (ENAC) Urban Transport Systems Laboratory (LUTS) Lausanne Switzerland

The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be studied in the spatiotemporal dimension. Investigating the clustering problem over time in the dynamic domain is critical to better understand and reveal the hidden information during the process of congestion formation and dissolution. The primary motivation of the paper is to study the spatiotemporal relation of congested links, observing congestion propagation from a macroscopic perspective, and finally identifying critical pockets of congestion that can aid the design of peripheral control strategies. To achieve this, we first introduce a static clustering method to partition the heterogeneous network into homogeneous connected sub-regions. This method guarantees connectivity of the cluster, which eases the development of a dynamic framework. The proposed clustering approach obtains a feasible set of connected homogeneous components in the network called snakes, which represent a sequence of connected links with similar level of congestion. Secondly, the problem is formulated as a mixed integer linear optimization to find major skeleton of clusters out of this feasible set by minimizing a heterogeneity index. Thirdly, a fine-tuning step is designed to assign the unclustered links of the network to proper clusters while keeping the connectivity. The approach is extended to capture spatiotemporal growth and formation of congestion. The dynamic clustering is based on an iterative and fast procedure that considers the spatiotemporal characteristics of congestion propagation and identifies the links with the highest degree of heterogeneity due to time dependent conditions and finally re-cluster them while by minimizing heterogeneity and imposing connectivity. The developed framework can be directly implemented in a

关键词： graph partitioning congestion propagation aggregate modeling mixed integer linear programming snake algorithm

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A snake-based scheme for path planning and control with constraints by distributed visual sensors

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ROBOTICA 2014年第4期32卷 477-499页

作者： Cheng, Y. Jiang, P. Hu, Y. F. Univ Bradford Sch Engn Design & Technol Bradford BD7 1DP W Yorkshire England Univ Hull Dept Comp Sci Kingston Upon Hull HU6 7RX N Humberside England

This paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment. Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is further proposed as a tracker of the reference path, taking into account the robot's non-holonomic constraint and limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance. Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control activities for a vehicle.

关键词： snake algorithm Wireless visual sensors Mobile robot navigation Distributed robot system

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LED-Induced fluorescence and image analysis to detect stink bug damage in cotton bolls

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JOURNAL OF BIOLOGICAL ENGINEERING 2013年第1期7卷 1-10页

作者： Mustafic, Adnan Roberts, Erin E. Toews, Michael D. Haidekker, Mark A. Univ Georgia Coll Engn Athens GA 30602 USA Univ Georgia Dept Entomol Tifton GA 31793 USA

Background: Stink bugs represent a major agricultural pest complex attacking more than 200 wild and cultivated plants, including cotton in the southeastern US. Stink bug feeding on developing cotton bolls will cause boll abortion or lint staining and thus reduced yield and lint value. Current methods for stink bug detection involve manual harvesting and cracking open of a sizable number of immature cotton bolls for visual inspection. This process is cumbersome, time consuming, and requires a moderate level of experience to obtain accurate estimates. To improve detection of stink bug feeding, we present here a method based on fluorescent imaging and subsequent image analyses to determine the likelihood of stink bug damage in cotton bolls. Results: Damage to different structures of cotton bolls including lint and carpal wall can be observed under blue LED-induced fluorescence. Generally speaking, damaged regions fluoresce green, whereas non-damaged regions with chlorophyll fluoresce red. However, similar fluorescence emission is also observable on cotton bolls that have not been fed upon by stink bugs. Criteria based on fluorescent intensity and the size of the fluorescent spot allow to differentiate between true positives (fluorescent regions associated with stink bug feeding) and false positives (fluorescent regions due to other causes). We found a detection rates with two combined criteria of 87% for true-positive marks and of 8% for false-positive marks. Conclusions: The imaging technique presented herein gives rise to a possible detection apparatus where a cotton boll is imaged in the field and images processed by software. The unique fluorescent signature left by stink bugs can be used to determine with high probability if a cotton boll has been punctured by a stink bug. We believe this technique, when integrated in a suitable device, could be used for more accurate detection in the field and allow for more optimized application of pest control.

关键词： Fluorescent Mark Needle Puncture Cotton Boll Fluorescent Region snake algorithm

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Videokymographic image processing: Objective parameters and user-friendly interface

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2012年第2期7卷 192-201页

作者： Manfredi, Claudia Bocchi, Leonardo Cantarella, Giovanna Peretti, Giorgio Univ Florence Dept Elect & Telecommun I-50139 Florence Italy Fdn IRCCS Osped Maggiore Policlin Mangiagalli & Regina Elen Dept Otolaryngol Milan Italy Spedali Civili Brescia Clin Otolaryngol Brescia Italy

Videolaryngostroboscopy (VLS) is undoubtedly a first choice examination technique in the diagnosis of several laryngeal pathologies. However, in case of low intensity or strong a-periodicity of the vocal sound, the VLS mechanism becomes ineffective in describing subsequent phases of the vocal cycle. To overcome such limitations, a new technique, called videokymography (VKG), was developed. VKG delivers images and displays the vibratory pattern from a single line selected from the whole VLS image, at the speed of approximately 8000 line-images/s. Despite its usefulness, parameter evaluation has been mostly based on visual inspection and no quantitative analysis of videokymographic images is commercially available at this time. This article presents the VKG-Analyser, a new tool for measuring and tracking quantitative parameters from VKG images. Specifically, the left-to-right period, amplitude and phase ratios and phase symmetry index were evaluated. The case of incomplete glottis closure, the minimum distance between folds was implemented. A digital image processing algorithm was developed and optimised for the analysis of VKG recordings that require intensity adjustment, noise removal and robust techniques for edge detection to avoid fluctuations of the grey levels in regions far from the vocal folds. The VKG-Analyser relies on a user-friendly interface that allows for the storage and retrieval of patients' data and optimises the image analysis, according to a set of parameters that can be manually adjusted by the user. It was successfully tested on a set of synthetic images and applied to real VKG images, both in the case of complete and incomplete glottis closure. The new software tool aims to provide fast, reliable and reproducible measures. When applied to a large set of data, it can define reference values for normal and pathological cases, providing a valid support for diagnosis and evaluation of surgical effectiveness. (C) 2011 Elsevier Ltd. All rights reserv

关键词： Vocal folds vibration Videokymographic images snake algorithm Greedy algorithm Contour detection Objective measures User-friendly interface

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