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A Novel Residual Attitude Estimation Approach Using Georeferenced Satellite imagery

Photogrammetric Engineering and Remote Sensing 2022年第10期88卷 631-641页

作者： Dubey, Bhaskar Kartikeyan, B. Image Analysis and Quality Evaluation Division of the Signal and Image Processing Group Space Applications Centre Indian Space Research Organisation Ahmedabad India

This article presents an efficient novel approach estimating residual attitude based on geometrically corrected (GEO) satellite images. A technique is presented that uses orbital plane geometry to compute the rotation angle as a function of geographic latitude between GEO image space and radiometrically corrected (RAD) image space. First, a nonlinear forward model is established that translates the residual errors in roll, pitch, and yaw to scan errors and pixel errors in GEO image space. Subsequently, the inverse problem is solved using Newton’s method of nonlinear optimization for estimating residual roll, pitch, and yaw. We demonstrate our results on data products of the high-resolution Indian satellites Cartosat-2E and Cartosat-2F. Further, the superiority of the proposed method is established by comparing it with multiple existing methods in the literature. The R2 measures of good-ness of fit for roll, pitch, and yaw estimation based on RAD and GEO products using the proposed method are 0.65, 0.99, and 0.65, respec-tively;using the existing method, they are 0.074, 0.005, and 0.50. © 2022 American Society for Photogrammetry and Remote Sensing.

关键词： Inverse problems

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Subpixel image Registration Using FFT-Based Technique 2nd

Subpixel Image Registration Using FFT-Based Technique

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2nd International Conference on Emerging Technologies for Computing, Communications, and Smart Cities, ETCCS 2021

作者： Jindal, Swati Dubey, Bhaskar Patel, Jitali Institute of Technology Nirma University Ahmedabad India Image Analysis and Quality Evaluation Division Signal and Image Processing Group Space Applications Centre Ahmedabad India

ISBN: (纸本)9789811902833

This paper presents an efficient image matching technique with translation, subpixel translation. Fast Fourier transform technique is the most powerful area-based technique that involves translation, rotation and other operation in frequency domain. FFT and its inverse are used to convert a two-dimensional image from the spatial domain to the frequency domain. The benefit of expressing an image in frequency domain is that executing certain operations on the frequencies is far more efficient than doing so in spatial domain. In this paper, we discussed different methods of subpixel estimation. Also, we validate the performance of our method under several different noise conditions. Our main goal to develop efficient technique to estimate translation and subpixel both by using FFT techniques. Our implementation is based on Python programming. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fast Fourier transforms

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First Results on Graph Similarity Search in Resting-State Functional Connectivity Networks Using Spectral and Graph Edit Distances

First Results on Graph Similarity Search in Resting-State Fu...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Carvalho, M.A.G. Frayne, R. School of Technology University of Campinas Brazil Radiology and Clinical Neurosciences Hotchkiss Brain Institute Univ. of Calgary Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary Canada Biomedical Engineering Graduate Program University of Calgary Canada Calgary Image Processing and Analysis Centre Foothills Medical Centre Calgary Canada

The application of graph theory in the modeling and analysis of brain networks has generated both new op portunities as well as new challenges in neuroscience. Resting state functional connectivity (RSFC) networks studied with graphs is an important field of investigation because of the potential benefits in understanding function in healthy individuals and identifying evidence of brain diseases and injury in patients. This work is unique because it applies information retrieval techniques to create ranked lists from RSFC graph theory derived networks. In our analysis, we used a sample of whole-brain resting-state functional magnetic reso nance imaging (rs-fMRI) data obtained from Young (n = 10, age: 20.1±2.1) and Old (n = 10, 65.6±0.4) sex-balanced groups drawn from a healthy, i.e., neurotypical, cohort. We estimated two well-known distance metrics (graph edit distance and graph spectral distance) and by using information-retrieval graph ranking methods achieved precision measures at the top-5 positions of ranked lists of up to 80%. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Graph neural networks

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Pilot Study of Distinct Graphs Models in analysis of Brain Aging in Resting-State Functional Connectivity Networks

Pilot Study of Distinct Graphs Models in Analysis of Brain A...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

Graphs have been used successfully to represent and analyze brain networks for many decades. Such structural and functional studies are important for revealing interactions between distinct areas of the brain that, for ex ample, are associated with the performance of a specific task or the onset of a cognitive disorder like dementia. In this pilot study, resting-state functional magnetic resonance imaging data were acquired in a sex-balanced sample of 10 young (20.1±2.1 years) and 10 old (65.6±0.4 years), presumed healthy, adults. We examined the effects of age on whole-brain resting-state functional connectivity (RSFC) networks. We examined two main graph modeling approaches to analyze RSFC networks. These approaches employ different strategies or graph models for thresholding over the complete network or examining changes in graph density. We com puted and compared one graph metric, the modularity, that was derived from the RSFC network graph models. Considering the need for a model that must preserve the network’s connectivity, strategies that use spanning trees as seeds to gradually increase the graph’s density seem more appropriate to represent brain networks. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Magnetic resonance imaging

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Assessment and comparison of image quality between two real-time sequences for dynamic MRI of distal joints at 3.0 Tesla (vol 64 , pg 1093 , 2023 )

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ACTA RADIOLOGICA 2023年第10期64卷 2686-2686页

作者： Garetier, M. Rousset, J. Makki, K. Department of Radiology Military Teaching Hospital Clermont-Tonnerre Brest FranceDepartment of Radiology University Hospital Morvan Brest FranceLaboratory of Medical Information Processing (LATIM) INSERM-UMR 1101 Brest France Department of Radiology Military Teaching Hospital Clermont-Tonnerre Brest France INRIA Fluminance Rennes FranceIFPEN Rueil-Malmaison France Laboratory of Medical Information Processing (LATIM) INSERM-UMR 1101 Brest FranceDepartment of Physical and Medical Rehabilitation University Hospital Morvan Brest FranceDepartment of Paediatric Physical and Medical Rehabilitation Fondation Ildys Brest FranceUniversity of Western Brittany (UBO) Brest France Laboratory of Medical Information Processing (LATIM) INSERM-UMR 1101 Brest FranceIMT Atlantique UBL Brest France Laboratory of Medical Information Processing (LATIM) INSERM-UMR 1101 Brest FranceUniversity of Western Brittany (UBO) Brest FranceDepartment of Radiology University Hospital La Cavale Blanche Brest France Laboratory of Medical Information Processing (LATIM) INSERM-UMR 1101 Brest FranceSymbiosis Centre for Medical Image Analysis Symbiosis International University Pune India

Background Real-time sequences allow functional evaluation of various joint structures during a continuous motion and help understand the pathomechanics of underlying musculoskeletal diseases. Purpose To assess and compare the image quality of the two most frequently used real-time sequences for joint dynamic magnetic resonance imaging (MRI), acquired during finger and ankle joint motion. Material and Methods A real-time dynamic acquisition protocol, including radiofrequency (RF)-spoiled and balanced steady-state free precession (bSSFP) sequences, optimized for temporal resolution with similar spatial resolution, was performed using a 3.0-T MRI scanner on 10 fingers and 12 ankles from healthy individuals during active motion. image quality criteria were evaluated on each time frame and compared between these two sequences. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were determined and compared from regions of interest placed on cortical bone, tendon, fat, and muscle. Visualization of anatomical structures and overall image quality appreciation were rated by two radiologists using a 0–10 grading scale. Results Mean CNR was significantly higher with bSSFP sequence compared to RF-spoiled sequence. The grading score was in the range of 5–9.3 and was significantly higher with RF-spoiled sequence for bone and joint evaluation and overall image appreciation on the two joints. The standard deviation for SNR, CNR, and grading score during motion was smaller with RF-spoiled sequence for both the joints. The inter-reader reliability was excellent (>0.75) for evaluating anatomical structures in both sequences. Conclusion A RF-spoiled real-time sequence is recommended for the in vivo clinical evaluation of distal joints on a 3.0-T MRI scanner.

关键词： Joints magnetic resonance imaging motion musculoskeletal system objective metrics quality

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Effect of blood pressure-lowering agents on microvascular function in people with small vessel diseases (TREAT-SVDs): a multicentre, open-label, randomised, crossover trial

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The Lancet Neurology 2023年第11期22卷 991-1004页

作者： Kopczak, Anna Stringer, Michael S van den Brink, Hilde Kerkhofs, Danielle Blair, Gordon W van Dinther, Maud Reyes, Carmen Arteaga Garcia, Daniela Jaime Onkenhout, Laurien Wartolowska, Karolina A Thrippleton, Michael J Kampaite, Agniete Duering, Marco Staals, Julie Lesnik-Oberstein, Saskia Muir, Keith W Middeke, Martin Norrving, Bo Bousser, Marie-Germaine Mansmann, Ulrich Rothwell, Peter M Doubal, Fergus N van Oostenbrugge, Robert Biessels, Geert Jan Webb, Alastair J S Wardlaw, Joanna M Dichgans, Martin Stringer, Michael S. Arteaga Reyes, Carmen Jaime Garcia, Daniela Thrippleton, Michael J. André, Elisabeth Kääb, Stefan Anders, Hans-Joachim Hack, Remco Kaffe, Maria Dewenter, Anna Malik, Rainer Institute for Stroke and Dementia Research University Hospital LMU Munich Munich Germany Centre for Clinical Brain Sciences University of Edinburgh Edinburgh United Kingdom UK Dementia Research Institute University of Edinburgh Edinburgh United Kingdom Department of Neurology UMC Utrecht Brain Center University Medical Center Utrecht Utrecht Netherlands Department of Neurology and School for Cardiovascular Diseases Maastricht University Medical Center+ Maastricht Netherlands Wolfson Centre for Prevention of Stroke and Dementia Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Medical Image Analysis Center and Department of Biomedical Engineering University of Basel Basel Switzerland Department of Clinical Genetics Leiden University Medical Center Leiden Netherlands School of Psychology and Neuroscience University of Glasgow Queen Elizabeth University Hospital Glasgow United Kingdom Hypertoniezentrum München Excellence Centre of the European Society of Hypertension Munich Germany Department of Clinical Sciences Lund Neurology Skåne University Hospital Lund University Lund Sweden Hôpital Lariboisière APHP Université Paris-Cité Paris France Institute for Medical Information Processing Biometry and Epidemiology LMU Munich Munich Germany Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases Munich Germany German Centre for Cardiovascular Research Munich Germany

Background: Hypertension is the leading risk factor for cerebral small vessel disease. We aimed to determine whether antihypertensive drug classes differentially affect microvascular function in people with small vessel disease. Methods: We did a multicentre, open-label, randomised crossover trial with blinded endpoint assessment at five specialist centres in Europe. We included participants aged 18 years or older with symptomatic sporadic small vessel disease or cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) and an indication for antihypertensive treatment. Participants were randomly assigned (1:1:1) to one of three sequences of antihypertensive treatment using a computer-generated multiblock randomisation, stratified by study site and patient group. A 2-week washout period was followed by three 4-week periods of oral monotherapy with amlodipine, losartan, or atenolol at approved doses. The primary endpoint was change in cerebrovascular reactivity (CVR) determined by blood oxygen level-dependent MRI response to hypercapnic challenge in normal-appearing white matter from the end of washout to the end of each treatment period. Efficacy analyses were done by intention-to-treat principles in all randomly assigned participants who had at least one valid assessment for the primary endpoint, and analyses were done separately for participants with sporadic small vessel disease and CADASIL. This trial is registered at ***, NCT03082014, and EudraCT, 2016-002920-10, and is terminated. Findings: Between Feb 22, 2018, and April 28, 2022, 75 participants with sporadic small vessel disease (mean age 64·9 years [SD 9·9]) and 26 with CADASIL (53·1 years [7·0]) were enrolled and randomly assigned to treatment. 79 participants (62 with sporadic small vessel disease and 17 with CADASIL) entered the primary efficacy analysis. Change in CVR did not differ between study drugs in participants with sporadic small vessel d

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Detecting Alzheimer’s Disease based on Structural Region analysis using a 3D Shape Descriptor

Detecting Alzheimer’s Disease based on Structural Region An...

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Brazilian Symposium on Computer Graphics and image processing (SIBGRAPI)

作者： Kauê T N Duarte David G Gobbi Richard Frayne Marco A G de Carvalho School of Technology University of Campinas Limeira SP Brazil Calgary Image Processing and Analysis Centre Hotchkiss Brain Institute University of Calgary Calgary AB Canada

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192758

Alzheimer's disease (AD) is a common neurodegenerative dementia that affects older people. Changes in behavior and cognition are the most common characteristics of this disease and are associated with changes in brain structure. Techniques focusing on brain shape have been recently proposed to quantify and understand these changes. One challenge when examining AD is that each anatomical region may have a unique role in and time course for brain deterioration, requiring a whole-brain method that is capable of individual (or regional) analyses at different disease stages. We propose to apply the scale-invariant heat kernel signature descriptor to magnetic resonance brain images in order to evaluate regional shape features across different brain regions. We measured the shape feature similarity in 500 subjects, equally divided across five progressive, disease-based stages. The shape analysis provided a complementary perspective to whole-brain analysis, due to the capability of identifying how different structures degenerate at different rates in the brain. In total, a group of 99 distinct brain regions belonging to cortical and deep gray matter were analyzed across the five disease stages. Preliminary assessment of shape-based analysis of key brain regions demonstrated that SIHKS was predictive of disease stage and disease progression.

关键词： Shape Feature extraction Diseases image segmentation Brain Training Tools

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Simultaneous Localization and Mapping Algorithm based on 3D Laser for Unmanned Aerial Vehicle 4th

Simultaneous Localization and Mapping Algorithm based on 3D ...

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4th International Conference on Electrical Engineering and Control Applications, ICEECA 2019

作者： Demim, Fethi Nemra, Abdelkrim Mouali, Oumima Hedir, Mehdia Rouigueb, Abdenebi Hamerlain, Mustapha Bendoumi, Mohamed Amine Bazoula, Abdelouahab Laboratoire des Véhicules Autonomes Intelligents Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Faculté des Sciences Mathématiques Physiques et Naturelles Université del Manar Tunis Tunisia Image Processing and Radiation Laboratory Faculty of Electronic and Informatics USTHB Bab Ezzouar Algiers Algeria Data Fusion and Analysis Laboratory Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Centre de Développement des Technologie Avancées CDTA Baba Hassen Algeria

ISBN: (纸本)9789811564024

This paper focuses on developing a robust solution for the Simultaneous Localization and Mapping (SLAM) problem to increase the autonomy of Unmanned Aerial Vehicles (UAV). The investigated topics are related to data fusion, localization, features extraction and matching, map building, 3D pose estimation and SLAM. One important aspect of the autonomous navigation which should be investigated as well as the fusion of data from the different sensor. The data fusion algorithms are very important and their performances are closely dependent on both performances of the constructed map and the accuracy of the UAV position within this map. Optimal and robust filter using centred Gaussian noises is implemented. The original contribution of this work is the proposition and the adaptation of the Extended Kalman Filter (EKF) to solve the Inertial Navigation Systems INS/3D laser UAV navigation problem. Simulation results for 3D flight scenario are presented to demonstrate the advantages of the hybrid localization based on EKF compared with results of the INS relative localization based technique. Good results were obtained with the EKF using noise process and/or measurement noise characteristics, particularly in the case of centred Gaussian noises. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Impact of scanner variability on lymph node segmentation in computational pathology

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Journal of Pathology Informatics 2022年 13卷 100127-100127页

作者： Khan, Amjad Janowczyk, Andrew Müller, Felix Blank, Annika Nguyen, Huu Giao Abbet, Christian Studer, Linda Lugli, Alessandro Dawson, Heather Thiran, Jean-Philippe Zlobec, Inti Institute of Pathology University of Bern Murtenstrasse 31 Bern CH-3008 Switzerland Case Western Reserve University Department of Biomedical Engineering Cleveland 44106 OH United States Department of Oncology Lausanne University Hospital and Lausanne University Lausanne Switzerland Swiss Federal Institute of Technology Lausanne (EPFL) Signal Processing Laboratory (LTS5) Lausanne Switzerland Institute of Complex Systems (iCoSyS) University of Applied Sciences and Arts Western Switzerland Delémont Switzerland Document Image and Video Analysis (DIVA) Research Group Department of Informatics University of Fribourg Fribourg Switzerland Department of Radiology Lausanne University Hospital Lausanne University Centre d'Imagerie Biomédicale (CIBM) Lausanne Switzerland Institute of Pathology City Hospital Triemli Zürich Switzerland

Computer-aided diagnostics in histopathology are based on the digitization of glass slides. However, heterogeneity between the images generated by different slide scanners can unfavorably affect the performance of computational algorithms. Here, we evaluate the impact of scanner variability on lymph node segmentation due to its clinical importance in colorectal cancer diagnosis. 100 slides containing 276 lymph nodes were digitized using 4 different slide scanners, and 50 of the lymph nodes containing metastatic cancer cells. These 400 scans were subsequently annotated by 2 experienced pathologists to precisely label lymph node boundary. Three different segmentation methods were then applied and compared: Hematoxylin-channel-based thresholding (HCT), Hematoxylin-based active contours (HAC), and a convolution neural network (U-Net). Evaluation of U-Net trained from both a single scanner and an ensemble of all scanners was completed. Mosaic images based on representative tiles from a scanner were used as a reference image to normalize the new data from different test scanners to evaluate the performance of a pre-trained model. Fine-tuning was carried out by using weights of a model trained on one scanner to initialize model weights for other scanners. To evaluate the domain generalization, domain adversarial learning and stain mix-up augmentation were also implemented. Results show that fine-tuning and domain adversarial learning decreased the impact of scanner variability and greatly improved segmentation across scanners. Overall, U-Net with stain mix-up (Matthews correlation coefficient (MCC) = 0.87), domain adversarial learning (MCC = 0.86), and HAC (MCC = 0.87) were shown to outperform HCT (MCC = 0.81) for segmentation of lymph nodes when compared against the ground truth. The findings of this study should be considered for future algorithms applied in diagnostic routines. © 2022 The Author(s)

关键词： Colorectal cancer Computational pathology Domain generalization Fine tuning Lymph node Lymph node segmentation Scanner variability Whole slide image

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Real-time Application of SLAM based-line for Unmanned ground vehicle 6

Real-time Application of SLAM based-line for Unmanned ground...

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6th International Conference on image and Signal processing and their Applications, ISPA 2019

作者： Demim, Fethi Nemra, Abdelkrim Hedir, Mehdia Rouigueb, Abdenebi Bazoula, Abdelouahab Hamerlain, Mustapha Ecole Militaire Polytechnique EMP Laboratoire des Véhicules Autonomes Intelligents Bordj El Bahri Algiers Algeria Faculty of Electronic and Informatics Image Processing and Radiation Laboratory Bab Ezzouar Algiers Algeria Ecole Militaire Polytechnique EMP Data Fusion and Analysis Laboratory Bordj El Bahri Algiers Algeria CDTA Centre de Developpement des Technologies Avancées Baba Hassen Algiers Algeria

ISBN: (纸本)9781728131566

This paper addresses the simultaneous localization and mapping (SLAM) of a robot in an unknown environment. Several techniques were proposed in the researches to solve this problem employ the different representations of the environment either by points or lines with their information on the extreme geometric positions. The objective of this paper has proposed a solution based on Extended Kalman Filter (EKF) for a single robot. The algorithm developed in our work is the EKF-SLAM which is implemented experimentally using a Pioneer 3-AT mobile robot equipped with a 2D laser telemeter. The obtained results using EKF-SLAM based lines shows the improvements in terms of accuracy and smoothness compared to the EKF-SLAM based points and odometer. © 2019 IEEE.

关键词： Mobile robots

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