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Author Correction: Federated learning enables big data for rare cancer boundary detection

Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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An approach for character segmentation of handwritten Bangla and devanagari script

An approach for character segmentation of handwritten Bangla...

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IEEE International Advance Computing Conference, IACC

作者： Anmol J Bhattad Bidyut B. Chaudhuri Electronics & Communication Engineering National Institute of Technology Karnataka Surathkal Mangalore India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479980482

In this paper a scheme for segmentation of unconstrained handwritten Devanagari and Bangla words into characters and its sub-parts is proposed. Firstly, the region above headline is identified by counting the number of white to black transitions in each row, which followed by its separation. Then the characters are segmented using fuzzy logic. For each column, the inputs to the fuzzy system are the location of first white pixel, thickness of the first black stroke, count of white pixels, and the run length count of white pixels.

关键词： Indexes Robustness

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Seeing the Sound: A New Multimodal Imaging Device for computer vision

Seeing the Sound: A New Multimodal Imaging Device for Comput...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Andrea Zunino Marco Crocco Samele Martelli Andrea Trucco Alessio Del Bue Vittorio Murino Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy Dipartimento DITEN Università degli Studi di Genova Genova Italy Pattern Anal. & Comput. Vision - Ist. Italiano di Tecnol. Genoa Italy Università degli Studi di Verona Verona Italy

ISBN: (纸本)9781467397124

Audio imaging can play a fundamental role in computer vision, in particular in automated surveillance, boosting the accuracy of current systems based on standard optical cameras. We present here a new hybrid device for acoustic-optic imaging, whose characteristics are tailored to automated surveillance. In particular, the device allows realtime, high frame rate generation of an acoustic map, overlaid over a standard optical image using a geometric calibration of audio and video streams. We demonstrate the potentialities of the device for target tracking on three challenging setup showing the advantages of using acoustic images against baseline algorithms on image tracking. In particular, the proposed approach is able to overcome, often dramatically, visual tracking with state-of-art algorithms, dealing efficiently with occlusions, abrupt variations in visual appearence and camouflage. These results pave the way to a widespread use of acoustic imaging in application scenarios such as in surveillance and security.

关键词： Acoustics Microphones Optical imaging Optical sensors Target tracking Array signal processing Visualization

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Concentration estimation of ultrasound targeted microbubbles for improving cancer detection: A preliminary in VIVO study

Concentration estimation of ultrasound targeted microbubbles...

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IEEE International Symposium on Biomedical Imaging

作者： Claudia Sciallero Andrea Trucco Dept. of Electrical Electronic Telecommunications Engineering and Naval Architecture (DITEN) University of Genoa Genova Italy Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia (IIT) Genova Italy

Ultrasound imaging can be combined with specific targeted contrast agents for noninvasive detection of pathology using disease-associated molecular signatures. The quantification of contrast agents effectively bound to receptors indicative of disease can provide additional information on the presence of pathology. This paper focuses on the opportunity of exploiting the echoes produced by targeted contrast agents to remotely estimate the volumetric concentration in a desired resolution cell. Tumor growth was induced by subcutaneous injection of prostate cancer cells in mice. A contrast-enhanced specific technique was used to image the region of interest subdivided in resolution cells of 1 mm-side. Radio frequency data were acquired for off-line processing. A method based on the support vector machine was employed to estimate the contrast agent concentration in each resolution cell. The preliminary in vivo experiments demonstrated a correlation between the extent of the primary tumor and the estimated concentrations. The combination of molecular imaging and contrast agent quantification provides the potential for earlier characterization of pathologies on molecular level.

关键词： Ultrasonic imaging Tumors Image resolution In vivo Estimation Molecular imaging

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Automated scoring of Bender Gestalt Test using image analysis techniques

Automated scoring of Bender Gestalt Test using image analysi...

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International Conference on Document Analysis and Recognition

作者： Momina Moetesum Imran Siddiqi Uzma Masroor Chawki Djeddi Center of Computer Vision and Pattern Recognition Bahria University Islamabad Pakistan Dept. of Professional Psychology Bahria University Islamabad Pakistan LAMIS Laboratory Larbi Tebessi University Tebessa Algeria

ISBN: (纸本)9781479918065

Drawing tests have been long used by practitioners and researchers for early detection of psychological and neurological impairments. These tests allow subjects to naturally express themselves as opposed to an interview or a written assessment. Bender Gestalt Test (BGT) is a well-known and established neurological test designed to detect signs of perceptual distortions. Subjects are shown a number of geometric patterns for reconstruction and assessments are made by observing properties like rotation, angulations, simplification and closure difficulty. The manual scoring of the test, however, is a time consuming and lengthy procedure especially when a large number of subjects is to be analyzed. This paper proposes the application of image analysis techniques to automatically score a subset of hand drawn images in the BGT test. A comparison of the scores reported by the automated system with those assigned by the psychologists not only reveals the effectiveness of the proposed system but also reflects the huge research potential this area possesses.

关键词： Manuals Psychology Biomedical imaging Computational modeling

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New texture-spatial features for keyword spotting in video images

New texture-spatial features for keyword spotting in video i...

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Asian Conference on pattern Recognition (ACPR)

作者： Palaiahnakote Shivakumara Guozhu Liang Sangheeta Roy Umapada Pal Tong Lu Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479961016

Keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure.

关键词： Image segmentation Semantics Video signal processing Indexing pattern recognition Spatial resolution

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Adaptive Local Movement Modelling for Object Tracking

Adaptive Local Movement Modelling for Object Tracking

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IEEE Workshop on Applications of computer vision (WACV)

作者： Baochang Zhang Zhigang Li Alessandro Perina Alessio Del Bue Vittorio Murino Istituto Italiano di Tecnologia Genova Liguria IT School of Automation Science and Electrical Engineering Beihang University Beijing China Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia Genova Italy

In this paper we present a novel strategy for modelling the motion of local patches for single object tracking that can be seamlessly applied to most part-based trackers in the literature. The proposed Adaptive Local Movement Modelling (ALMM) method is able to model the local spatial distribution of the image patches defining the object to track and the reliability of each image patch. Given the output of a base tracking algorithm, a Gaussian Mixture Model (GMM) is first used to model the distribution of the movement of local patches relative to the gravity center of the tracked object. Then, the GMM is combined with the base tracker in a boosting framework, which gives a novel integrated boosting classifier for the tracking task. This provides a robust procedure to detect outliers in the local motion of the patches. The algorithm is highly configurable with the possibility to change the number of local patches used for tracking and to adapt to the variations of the tracked object. Tracking results on standard datasets show that equipping state-of-the-art trackers with our tehcnique remarkably improves their performance.

关键词： Target tracking Gravity Adaptation models Boosting Computational modeling Robustness

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3D reconstruction based on light field information

3D reconstruction based on light field information

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International Conference on Information and Automation (ICIA)

作者： Yan Zhou Huiwen Guo Ruiqing Fu Guoyuan Liang Can Wang Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781467391054

As an important branch of computational photography, light field photography combines the hardware design of optical system with key algorithm of signal processing quite well. Unlike traditional photography which can only record light ray's two-dimensional position, light field photography system can record four-dimensional position and direction. Therefore, much more image information can be obtained from light field photography. With the development of 3D display technology, light field based autofocus and 3D display technology is becoming more and more popular. In this paper, a light field based new 3D reconstruction algorithm for buildings and office environment is proposed by applying Wavelet Transform and SVM (Support Vector Machine) model to obtain the image focusing quality assessment, along with the Mean Shift Algorithm and Random Field Model to get the depth map of the scene. Firstly, light field image is captured by using a light field camera. Secondly, we use frequency domain digital refocus algorithm to manipulate light field image and obtain several serialized refocused images with different focus. Thirdly, wavelet features are extracted from each refocused image, and then an image focusing quality assessment is conducted by using RBF (Radial Basis Function) kernel based SVM model. Finally, we use Mean Shift algorithm to realize color clustering of the original light field image, and then build MRF (Markov Random Field) Model with color nodes. By iterating the likelihood depth result obtained from real scenario depth calibrations according to image focusing quality assessment, finally the depth map of the scene is reconstructed. Experiments are conducted to prove the feasibility of the proposed 3D reconstructed algorithm based on light field. And the experimental results on real datasets demonstrate good performance of this algorithm.

关键词： Three-dimensional displays Cameras Photography Lenses Wavelet transforms Focusing

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A new method based on bag of filters for character recognition in scene images by learning

A new method based on bag of filters for character recogniti...

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International Conference on Document Analysis and Recognition

作者： Qisu Li Tong Lu Palaiahnakote Shivakumara Umapada Pal Chew Lim Tan National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India School of Computing National University of Singapore Singapore

ISBN: (纸本)9781479918065

Achieving a good recognition rate for scene characters is a big challenge due to non-uniform illumination effects, perspective distortions, multiple colors or contrasts, different fonts and their various sizes, background or orientation variations, etc. Unlike the existing recognition methods that use binary information or the features extracted from different domains, the proposed method explores gray information in the form of a filter bank to extract the discriminative power for all the 62 scene character classes. We propose a sliding window (patch) operation over a character image for learning the global features, which represent the structures of character images of all the classes by reconstructing a filter bank from the original data. We introduce shareable constrains to activate class-specific filters from the filter bank. Further, we propose constraints by studying the nearest neighbor patches and exemplar selection to maximize the gap between inter-classes and minimize the gap between intra-classes. The method is evaluated and compared with several existing recognition methods in terms of character recognition rate. Experimental results show that the proposed method outperforms the existing methods.

关键词： Character recognition Image segmentation Filter banks Accuracy

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Erratum to: Crowd behavior representation: an attribute-based approach

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SpringerPlus 2016年第1期5卷 2114页

作者： Hamidreza Rabiee Javad Haddadnia Hossein Mousavi Moin Nabi Vittorio Murino Nicu Sebe Electrical Engineering Department Hakim Sabzevari University Sabzevar Iran. Biomedical Engineering Department Hakim Sabzevari University Sabzevar Iran. Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia Genoa Italy. DISI University of Trento Trento Italy.

[This corrects the article DOI: 10.1186/s40064-016-2786-0.].

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