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Common Carotid Artery Lumen Segmentation from Cardiac Cycle-resolved Cine Fast Spin Echo Magnetic Resonance Imaging 30

Common Carotid Artery Lumen Segmentation from Cardiac Cycle-...

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30th SIBGRAPI Conference on graphics, Patterns and images Tutorials (SIBGRAPI-T)

作者： Rodrigues, Livia Rittner, Leticia Lotufo, Roberto Souza, Roberto Frayne, Richard Univ Estadual Campinas Fac Elect & Comp Engn Med Image Comp Lab Campinas SP Brazil Univ Calgary Hotchkiss Brain Inst Dept Radiol & Clin Neurosci Calgary AB Canada

ISBN: (纸本)9781538622193

Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging is a non-invasive method that can be used to analyze the carotid artery and detect signs of atherosclerosis. Most MR methods acquire high contrast, static images. These methods, however, are sensitive to artifacts from cardiac motion, produce time-averaged images, and do not allow for carotid distensibility analysis. Carotid distensibility is an important, systematic measure of vascular health. Cine fast spin echo (FSE) is a new MR imaging that can obtain dynamic MR data (i.e., cardiac phase-resolved datasets). Dynamic imaging, however, comes at the cost of lower spatial resolution and signal-to-noise ratio, making these data potentially more difficult to segment. This paper introduces a semi-automated segmentation method that segments the common carotid artery (CCA) lumen across the cardiac cycle from dynamic MR images. To the best of our knowledge, this work is the first proposed technique for segmenting cardiac cycle-resolved cine FSE images. It combines a priori knowledge about the size and shape of the CCA, with the max-tree data structure, the tie-zone watershed transform (using identified internal and external markers) and supervised classification, to segment the carotid artery wall-lumen boundary. The user has to select only a seed point (centred in the carotid artery lumen). Technique performance was assessed using forty-five cine FSE data sets, each consisting of images reconstructed at sixteen temporal bins across the cardiac cycle. The automatic segmentation results were compared against the consensus of three different manual segmentation results. Our technique achieved an average Dice coefficient, sensitivity and false positive rate of 0.928 +/- 0.031 (mean +/- standard deviation), 0.915 +/- 0.037 and 0.056 +/- 0.049, respectively. Our method achieved higher agreement versus the consensus of the three man

关键词： max-tree watershed transform carotid artery imaging carotid artery segmentation carotid artery distensibility cine FSE

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Analysis of the Influence of Color Normalization in the Classification of Non-Hodgkin Lymphoma images

Analysis of the Influence of Color Normalization in the Clas...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Matheus G. Ribeiro Leandro A. Neves Guilherme F. Roberto Thaína A. A. Tosta Alessandro S. Martins Marcelo Z. do Nascimento Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) São Paulo Brazil Universidade Federal de Uberlandia Uberlandia MG BR Center of Mathematics Computing and Cognition Federal University of ABC Santo André Uberlândia Brazil Federal Institute of Triangulo Mineiro (IFTM) Ituiutaba Minas Gerais Brazil Faculty of Computation (FACOM) Federal University of Uberlândia (UFU) Uberlândia Minas Gerais Brazil

In this work, a method is proposed to analyze the influence of color normalization in the classification lymphoma images. The approach combines multidimensional fractal techniques, curvelet transforms and Haralick features. The method considered a feature selection technique and different classification approaches to evaluate the combinations, such as decision tree, random forest, support vector machine, naive bayes and k-star. The classifications were analyzed considering three common lymphoma classes: mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia. The best result was achieved for the extraction from input images, features obtained mostly from lacunarity and percolation from curvelet sub-images, using random forest classifier. The tests were considered with 10-fold cross-validation and the result was a rate of AUC=0.963. The color normalization was not able to provide relevant classification rates. The obtained performance with the analysis over different types of features, classifiers and color normalization influence are important contributions to the identification of the lymphoma cancer.

关键词： Feature extraction image color analysis Fractals Transforms Cancer Probabilistic logic Support vector machines

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Precise Edge Tracking of Vehicles Using a Static Camera Setup

Precise Edge Tracking of Vehicles Using a Static Camera Setu...

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IEEE International symposium on Intelligent Signal processing and Communication Systems (ISPACS)

作者： Stephan Ihrke Fraunhofer IVI Fraunhofer Institute for Transportation and Infrastructure Systems Dresden Germany

We describe a novel approach to highly accurate model-based vehicle tracking using multiple static cameras. The proposed system is able to cope with complex lighting situations by using a particle filtering framework based on edge features. The correct pose is determined by rendering multiple pose hypotheses of the 3D model and comparing the observation with the rendering. The main contribution of this work is the adaptation of the edge-based particle filter to the domain of vehicle tracking by exploiting the prevalent constraints. The degrees of freedom of vehicle motion can be reduced to three by taking advantage of the ground plane constraint. Furthermore, a vehicle motion model is employed to reduce the number of probable poses, thus increasing accuracy and speed. We exploit graphics processing unit functionalities to achieve real-time operation.

关键词： Cameras image edge detection Solid modeling Target tracking Rendering (computer graphics) Three-dimensional displays

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Deep Instance Segmentation of Teeth in Panoramic X-Ray images

Deep Instance Segmentation of Teeth in Panoramic X-Ray Image...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Gil Jader Jefferson Fontineli Marco Ruiz Kalyf Abdalla Matheus Pithon Luciano Oliveira Intelligent Vision Research Lab Federal University of Bahia Southeast State University of Bahia

In dentistry, radiological examinations help specialists by showing structure of the tooth bones with the goal of screening embedded teeth, bone abnormalities, cysts, tumors, infections, fractures, problems in the temporomandibular regions, just to cite a few. Sometimes, relying solely in the specialist's opinion can bring differences in the diagnoses, which can ultimately hinder the treatment. Although tools for complete automatic diagnosis are no yet expected, image pattern recognition has evolved towards decision support, mainly starting with the detection of teeth and their components in X-ray images. Tooth detection has been object of research during at least the last two decades, mainly relying in threshold and region-based methods. Following a different direction, this paper proposes to explore a deep learning method for instance segmentation of the teeth. To the best of our knowledge, it is the first system that detects and segment each tooth in panoramic X-ray images. It is noteworthy that this image type is the most challenging one to isolate teeth, since it shows other parts of patient's body (e.g., chin, spine and jaws). We propose a segmentation system based on mask region-based convolutional neural network to accomplish an instance segmentation. Performance was thoroughly assessed from a 1500 challenging image data set, with high variation and containing 10 categories of different types of buccal image. By training the proposed system with only 193 images of mouth containing 32 teeth in average, using transfer learning strategies, we achieved 98% of accuracy, 88% of F1-score, 94% of precision, 84% of recall and 99% of specificity over 1224 unseen images, results very superior than other 10 unsupervised methods.

关键词： Teeth image segmentation Dentistry X-ray imaging Training image restoration Feature extraction

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The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments

The Impact of Preprocessing on Deep Representations for Iris...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Luiz A. Zanlorensi Eduardo Luz Rayson Laroca Alceu S. Britto Luiz S. Oliveira David Menotti Department of Informatics Federal University of Paraná (UFPR) Curitiba PR Brazil Computing Department Federal University of Ouro Preto (UFOP) Ouro Preto MG Brazil Postgraduate Program in Informatics Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil

The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the NICE. II competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.

关键词： Iris recognition Databases image segmentation Feature extraction Deep learning Protocols image recognition

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FPGA BASED IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR HYPERSPECTRAL CLASSIFICATION

FPGA BASED IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK FO...

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IEEE International Geoscience and Remote Sensing symposium

作者： Xiaofeng Chen Jingyu Ji Shaohui Mei Yifan Zhang Manli Han Qian Du School of Electronics and Information Northwestern Polytechnical University Xi'an China Aeronautical Computing Technique Research Institute Xian China Department of Electrical and Computer Engineering Mississippi State University MS USA

convolutional neural network (CNN) has been widely used for hyperspectral classification. Current researches of CNN based hyperspectral image classification is mainly implemented on graphics processing unit (GPU) platform. However, GPU is not suitable for onboard processing due to the problem of space radiation and power supply on image acquiring platform. Therefore, in this paper, FPGA is selected to implement CNN based hyperspectral classification for further onboard processing. Specially, a hardware model is designed for the forward classification step of CNN using hardware description language, including computation structure for CNN, implementation of different layers, weight loading scheme, and data interfere. Simulation results over Pavia data set validate the proposed FPGA based implementation is coincide with that on GPU platform.

关键词： Field programmable gate arrays Hyperspectral imaging graphics processing units Convolution Computational modeling Feature extraction

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Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

Robust Iris Segmentation Based on Fully Convolutional Networ...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Cides S. Bezerra Rayson Laroca Diego R. Lucio Evair Severo Lucas F. Oliveira Alceu S. Britto David Menotti Federal University of Paraná (UFPR) Curitiba PR Brazil Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil

The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.

关键词： image segmentation Iris recognition Iris Generative adversarial networks Gallium nitride Biomedical imaging Feature extraction

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GPU ACCELERATION OF UAV image SPLICING USING ORIENTED FAST AND ROTATED BRIEF COMBINED WITH PCA

GPU ACCELERATION OF UAV IMAGE SPLICING USING ORIENTED FAST A...

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IEEE International Geoscience and Remote Sensing symposium

作者： Chia-Cheng Yeh Yang-Lang Chang Pai-Hui Hsu Cheng-Huan Hsien Department of Electrical Engineering National Taipei University of Technology Department of Civil Engineering National Taiwan University

In this study, an accelerating method of oriented FAST and rotated BRIEF combined with principal component analysis (ORB/PCA) is proposed for splicing detection of unmanned aerial vehicle (UAV) images. Compared to traditional scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods, the proposed ORB/PCA can not only be faster but also produce more accurate. Moreover, in order for the proposed ORB to be effective for image stitching process in near real-time, the Compute Unified Device Architecture (CUDA) application programming interface of graphics processing unit (GPU) is cooperated to speed up the proposed method. Experimental results show that the proposed GPU based ORB/PCA framework is suitable for splicing detection of UAV images in Earth remote sensing. It can improve the image stitching process both in time and accuracy compared to conventional methods.

关键词： Feature extraction graphics processing units Principal component analysis Splicing Unmanned aerial vehicles computer vision

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Research of image Preprocessing in the Recognition System of RMB Crown Word 13th

Research of Image Preprocessing in the Recognition System of...

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13th International symposium on Smart graphics (SG)

作者： Zhang, GuangJian Sichuan Coll Architectural Technol Dept Informat Engn Deyang Sichuan Peoples R China

ISBN: (纸本)9783319538389;9783319538372

A crown word on a Chinese RMB note is a sequence of characters that uniquely identify a bank note. Training set sampling is very important in the recognition system of RMB crown word, which directly affects the accuracy of the recognition system. image preprocessing operations require a number of properties on the crown characters such as the location, tilt correction, attitude, adjustment, noise reduction, binarization and single character segmentation and so on.

关键词： image processing image segmentation The RMB crown word Pattern recognition

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Automated Generation of HDL Implementations of Dadda and Wallace Tree Multipliers 7

Automated Generation of HDL Implementations of Dadda and Wal...

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7th brazilian symposium on Computing Systems Engineering (SBESC)

作者： de Castro, Lucas G. Ogawa, Henrique S. Albertini, Bruno de C. Univ Sao Paulo Escola Politecn Sao Paulo SP Brazil

ISBN: (纸本)9781538635902

Convolutional Neural Networks are being studied to provide features such as real time image recognition. One of the key operations to support HW implementations of this type of network is the multiplication. Despite the high number of operations required by Convolutional Neural Networks, they became feasible in the past years due the high availability of computing power, present on devices such as Graphic processing Units. However, those implementations are unsuitable for energy constrained scenarios, such as embedded devices. FPGAs are programmable devices that are being considered as a low power alternative for GPUs. This work proposes and implement a generator of two fast combinatorial multipliers: Dadda and Wallace tree. Our generator is capable of generating structural descriptions of both designs for any operand width, an operation considered unfeasible by hand. We evaluated our generator using two low-cost FPGA platforms, easily found on the market.

关键词： Field programmable gate arrays Hardware design languages Delays Complexity theory Hardware Logic gates graphics processing units

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