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Preface - Special issue on advanced technology and application of H.264/AVC

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FUJITSU SCIENTIFIC & TECHNICAL JOURNAL 2008年第3期44卷 341-342页

作者： Matsuda, Kiichi Research Center for Image Processing and Biometrics Technologies Fujitsu Laboratories Ltd.

No abstract available

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Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation

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Scientific Reports 2025年第1期15卷 1-20页

作者： Sajed Rakhshani Mahnoosh Tajmirriahi Farnaz Sedighin Hossein Rabbani Amirali Arbab Aref Habibi Mohsen Pourazizi Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Ophthalmology Isfahan Eye Research Center Isfahan University of Medical Sciences Isfahan Iran

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in the hybrid integration of data-driven deep-learning with a model-informed noise representation, specifically designed to address the very low signal-to-noise ratio (SNR) and low-resolution challenges in SS-OCT imaging. SDNet introduces a two-step training process, leveraging noise-free OCT references to simulate low-SNR conditions. In the first step, the network learns to enhance noisy images by combining denoising and super-resolution within noise-corrupted reference domain. To refine its performance, the second step incorporates Principle Component Analysis (PCA) as self-supervised denoising strategy, eliminating the need for ground-truth noisy image data. This unique approach enhances SDNet’s adaptability and clinical relevance. A key advantage of SDNet is its ability to balance contrast-texture by adjusting the weights of the two training steps, offering clinicians flexibility for specific diagnostic needs. Experimental results across diverse datasets demonstrate that SDNet surpasses traditional model-based and data-driven methods in computational efficiency, noise reduction, and structural fidelity. The framework excels in improving both image quality and diagnostic accuracy. Additionally, SDNet shows promising adaptability for analyzing low-resolution, low-SNR OCT images, such as those from patients with diabetic macular edema (DME). This study establishes SDNet as a robust, efficient, and clinically adaptable solution for OCT image enhancement addressing critical limitations in contemporary imaging workflows.

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A New Texture-Based Segmentation Method for Optical Coherence Tomography images 41

A New Texture-Based Segmentation Method for Optical Coherenc...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

作者： Monemian, Maryam Rabbani, Hossein Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

ISBN: (纸本)9781538613115

Optical Coherence Tomography (OCT) is an imaging modality which facilitates capturing pictures from biological organs like retina. Accurate segmentation and verification of OCT images leads to the identification and treatment of harmful retinal diseases such as glaucoma. The main fact used for segmentation in this paper is that a considerable number of boundary pixels have similar features from texture point-of-view. Thus, a novel low-complexity segmentation method for OCT images is proposed paying attention to the texture feature of pixels on the boundaries. The simulation results show that the proposed method provides acceptable values for mean signed and unsigned errors compared to the result of manual segmentation. © 2019 IEEE.

关键词： Optical tomography

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Corpus Callosum Indices are Correlated with Disability and Cognitive Performance in Patients with Multiple Sclerosis: A Systematic Review and -Analysis

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Multiple Sclerosis and Related Disorders 2025年

作者： Farinaz Tabibian Elahe Hoseini Ali Motahharynia Kiarash Azimzadeh Raheleh Kafieh Zahra Amini Iman Adibi Neurosciences Research Center Isfahan University of Medical Sciences Isfahan Iran School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Engineering Durham University United Kingdom Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran Department of Neurology Isfahan University of Medical Sciences Isfahan Iran

Corpus callosum (CC) is a key biomarker in multiple sclerosis (MS), with indices such as CC volume (CCV) and CC area (CCA) being correlated with disability and cognitive performance. However, discrepancies remain in the comparative associations of these indices and among MS phenotypes. A systematic search identified studies on MS patients, where CC size or volume measures were correlated with clinical outcomes, including Expanded Disability Status Scale (EDSS), Symbol Digit Modalities Test (SDMT), or Paced Auditory Serial Addition Test. The relationship between a CC index and a clinical outcome, where the correlation coefficient was reported in three or more studies, was synthesized using random-effects models. Risk of bias, reporting bias and certainty of analyses were evaluated. Subgroup analyses and meta-regression explored the heterogeneity. According to 35 included studies, among overall MS population, normalized CCV (nCCV) and CCA (nCCA) were the strongest correlates of EDSS (r = -0.383) and SDMT (r = 0.600), respectively, when controlling for demographic characteristics using partial correlation. Correlation of nCCA with EDSS was modulated by age (R^2 = 0.42), while it was absent in the study that included only relapsing-remitting MS (RRMS). In RRMS patients, significant association was observed between nCCV and EDSS (r = -0.429). Association between nCCV and SDMT (r = 0.226) was strengthened with increased disease duration (R^2 = 0.57) and in primary progressive MS patients (R^2 = 0.23). CCV reflects progression of disability in RRMS, whereas in progressive MS CCV marks cognitive decline and CCA could reflect the disability. This emphasizes the potential of CC indices to monitor the disease impact.

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End-to-End Spectral-Temporal Fusion Using Convolutional Neural Network 1

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4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2020

作者： Benzenati, Tayeb Kallel, Abdelaziz Kessentini, Yousri Digital Research Center of Sfax Sfax3021 Tunisia Advanced Technologies for Image and Signal Processing University of Sfax Sfax Tunisia MIRACL Laboratory University of Sfax Sfax Tunisia

ISBN: (数字)9783030718046

ISBN: (纸本)9783030718039

In the last few years, Earth Observation sensors received a large development, offering, therefore, various types of data with different temporal, spatial, spectral, and radiometric resolutions. However, due to physical and budget limitations, the acquisition of images with the best characteristics is not feasible. image fusion becomes a valuable technique to deal with some specific applications. In particular, the vegetation area, which needs a high spectral resolution and frequent coverage. In this paper, we present a novel fusion technique based on Convolutional Neural Networks (CNN) to combine two kinds of remote sensing data with different but complement spectral and temporal characteristics, to produce one high spectral and temporal resolution product. To the best of our knowledge, this is the first attempt to deal with the spectral-temporal fusion problem. The feasibility of the proposed method is evaluated via Sentinel-2 data. The experimental results show that the proposed technique can achieve substantial gains in terms of fusion performance. © 2021, Springer Nature Switzerland AG.

关键词： image fusion

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Total variation regularized tensor ring decomposition for OCT image denoising and super-resolution

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Computers in Biology and Medicine 2024年 177卷 108591-108591页

作者： Ghaderi Daneshmand, Parisa Rabbani, Hossein Medical Image & Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan*** Iran

This paper suggests a novel hybrid tensor-ring (TR) decomposition and first-order tensor-based total variation (FOTTV) model, known as the TRFOTTV model, for super-resolution and noise suppression of optical coherence tomography (OCT) images. OCT imaging faces two fundamental problems undermining correct OCT-based diagnosis: significant noise levels and low sampling rates to speed up the capturing process. Inspired by the effectiveness of TR decomposition in analyzing complicated data structures, we suggest the TRFOTTV model for noise suppression and super-resolution of OCT images. Initially, we extract the nonlocal 3D patches from OCT data and group them to create a third-order low-rank tensor. Subsequently, using TR decomposition, we extract the correlations among all modes of the grouped OCT tensor. Finally, FOTTV is integrated into the TR model to enhance spatial smoothness in OCT images and conserve layer structures more effectively. The proximal alternating minimization and alternative direction method of multipliers are applied to solve the obtained optimization problem. The effectiveness of the suggested method is verified by four OCT datasets, demonstrating superior visual and numerical outcomes compared to state-of-the-art procedures. © 2024 Elsevier Ltd

关键词： Optical tomography

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Principal line based ICP alignment for palmprint verification

Principal line based ICP alignment for palmprint verificatio...

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IEEE International Conference on image processing

作者： Wei Li Lei Zhang David Zhang Jingqi Yan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Biometrics Research Center Department of Computing Hong Kong Polytechnic University Hong Kong China

image alignment is a crucial step in palmprint verification. However, most of the existing palmprint alignment methods use only some key points between fingers or in palm boundary to extract the region of interest (ROI), which is consequently used for feature extraction and matching. Such alignment methods can only give a coarse alignment of the palmprint images. This paper presents a new effective refinement method for palmprint alignment by adapting the iterative closest point (ICP) method to the palmprint principal lines. The proposed method offers a more accurate alignment of palmprints by correcting efficiently the shifting, rotation and scaling variations introduced in data acquisition. The experimental results show that the proposed method can greatly improve the palmprint verification accuracy in real time.

关键词： Iterative closest point algorithm biometrics Fingers Feature extraction Data mining Iterative methods Authentication Shape image processing Pattern recognition

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Three dimensional palmprint recognition

Three dimensional palmprint recognition

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wei Li Lei Zhang David Zhang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Biometrics Research Center Department of Computing Hong Kong Polytechnic University Hong Kong China

Palmprint has been widely studied as its high accuracy and low cost. Most of the previous studies are based on two dimensional (2D) image of the palmprint. However, 2D image can be easily forged, which will threaten the security of palmprint authentication system. Furthermore, 2D image can be easily affected by noise, such as scrabbling and dirty in the palm. To overcome these shortcomings, we develop a three dimensional (3D) palmprint identification system. The structured-light imaging technology is adopted to collect the 3D palmprint data, from which the stable mean curvature image (MCI) is extracted. Then the competitive coding (CompCode) technique is used to code the 3D palmprint pattern according the MCI. By using score level fusion of MCI and its CompCode, promising recognition performance is achieved on our established 3D palmprint database.

关键词： biometrics Authentication Pattern recognition image recognition Data acquisition Time measurement Surface reconstruction image reconstruction Costs Data security

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Efficient Joint 2D and 3D Palmprint Matching with Alignment Refinement

Efficient Joint 2D and 3D Palmprint Matching with Alignment ...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Wei Li Lei Zhang David Zhang Guangming Lu Jingqi Yan Biometrics Research Center Dept. of Computing The Hong Kong Polytechnic University Biocomputing Research Center Shenzhen Graduate School Harbin Institute of Technology Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781424469840;9781424469857

Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.

关键词： Dermatoglyphics Three Dimensions

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Automatic detection of Hyperreflective Foci in optical coherence tomography B-scans using Morphological Component Analysis 39

Automatic detection of Hyperreflective Foci in optical coher...

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39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

作者： Mokhtari, Marzieh Ghasemi Kamasi, Zeinab Rabbani, Hossein Department of Bioelectric and Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences *** Iran Lane Department of Computer Science and Electrical Engineering West Virginia University 26506 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences *** Iran

ISBN: (纸本)9781509028092

Hyperreflective Foci (HF) is one of the most common complications distributed in cross-sectional images of patients with Diabetic Macular Edema (DME). Scanning Laser Ophthalmoscope (SLO) images usually consists of several B-scans that represent a cross-sectional reconstruction of a plane through the anterior or posterior regions of retina. In each B-scan, HFs are geometrically distinct constituents in different retinal layers. Since the intensity levels of HFs and many other subjects in B-scans are the same, in this paper we try to separate HFs from other objects by detection of the point and curve singularities in each B-scan. The decomposition algorithm presented in this paper is based on sparse image representation of B-scans using Morphological Component Analysis (MCA) technique. By using curvelet transform and Daubechies wavelet basis, two different over-complete dictionaries are constructed which represent two various aspects of B-scans. The HFs are more distinguished in reconstructed image with wavelet dictionary and other objects are mostly detectable by curvelet dictionary. So, HFs can be detected by applying an optimum threshold criterion on reconstructed image by wavelet atoms. Finally, the false positive points are reduced by removing the candidate points in RNFL and RPE layers, which are automatically segmented based on ridgelet transform. Our simulation results on 1924 HFs show that sensitivity and specificity for HF detection is 91.0% and 100%, respectively. © 2017 IEEE.

关键词： Dictionaries Retina Hafnium image reconstruction Wavelet transforms Diseases

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