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Segmentation and region quantification of bubbles in small bowel capsule endoscopy images using wavelet transform

Informatics in Medicine Unlocked 2023年 42卷

作者： Sadeghi, Vahid Vard, Alireza Sharifi, Mohsen Mir, Hossein Mehridehnavi, Alireza Medical Image & Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Bioelectrics and Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Gastroenterologist & Hepatologist Fellowship of Endosonography Isfahan University of Medical Sciences Isfahan Iran

Objective: A large number of captured frames by the wireless capsule endoscopy have been contaminated with different amounts of bubbles. Bubbles can degrade the visualization quality of the small intestine mucosa. The aim of this study is to develop an objective method for evaluating the amount of bubbles in WCE images. Methods: Frames with varying levels of bubble occlusion were selected from the Kvasir capsule endoscopy dataset. The round shape bubbles have an edge in their boundaries. Edges in the spatial domain correspond to high-frequency bands in the frequency domain. Two automated edge detection approaches have been developed in a rule-based manner and evaluated to assess the amount of bubbles. The first approach involved high pass filtering using fast Fourier transform (FFT), while the second approach has been based on wavelet image decomposition and reconstruction by omitting approximation coefficients subband. Results: Both Fourier and wavelet transforms obtained approximately the same dice similarity score (DSC), and precision metrics, which were equal to 0.87, and 0.91, respectively. Based on the specificity measure, the FFT outperformed the Hough and wavelet transforms. However, the wavelet transform obtained a higher dice similarity score (DSC) (0.93), accuracy (0.95), and sensitivity metric (0.97) and was the fastest, with an execution time of 0.01 s per frame, making it suitable for real-time applications. Conclusion: The proposed technique provides an easy-to-implement method for quality reporting or objective comparison tools of different bowel preparation paradigms in real-time applications due to its fast execution time. The obtained results from two different datasets proved that the presented method has good generalization. © 2023

关键词： Bubble Fourier transform Small bowel Wavelet transform Wireless capsule endoscopy

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Clove leaf oil purification with citric acid activated bentonite clay adsorbent from kaffir lime (Citrus hystrix DC)

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AIP Conference Proceedings 2023年第1期2727卷

作者： Aprelia Putri Verdianti Kun Harismah Malik Mustofa Mahmoud Mirzaei Muhammadiyah University of Surakarta Chemical Engineering Surakarta Indonesia Medical Image & Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

The adsorption of metals by citric acid-activated clay from kaffir lime (Citrus hystrix DC) has been evaluated for its adsorption in the purification of clove leaf oil. This study at to measure the effectiveness of bentonite clay adsorbent activated by citric acid from kaffir lime and reduce Fe metal content in clove leaf oil. Purification is carried out in 3 stages, the first stage is the activation of bentonite clay with citric acid from kaffir lime with a ratio of 1: 7 for 24 hours. The second stage is the purification of clove leaf oil by adsorption method using activated bentonite clay as an adsorbent, the ratio between bentonite clay and clove leaf oil is 1:20 and adsorbed for (90; 105; 120; and 135) minutes. The next step is product analysis which includes organoleptic, physical, and Fe content analysis using the Atomic Absorption Spectrophotometry method. From the results of the study, it was concluded that citric acid activated bentonite clay from kaffir lime was effective in absorbing Fe metal from clove leaf oil content of 20.98 ppm to <0.04 ppm with an adsorption time of 120 minutes, and the results of organoleptic and physical tests according to SNI oil clove leaf.

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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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A Theoretical Comparison among Recursive Algorithms for Fast Computation of Zernike Moments Using the Concept of Time Complexity

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American Journal of Computational Mathematics 2021年第4期11卷 304-326页

作者： Nasrin Bastani Alireza Vard Mehdi Jabalameli Vahid Bastani School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran Computer Department Islamic Azad University Najafabad Branch Isfahan Iran English Department Islamic Azad University Najafabad Branch Isfahan Iran

Zernike polynomials have been used in different fields such as optics, astronomy, and digital image analysis for many years. To form these polynomials, Zernike moments are essential to be determined. One of the main issues in realizing the moments is using factorial terms in their equation which causes higher time complexity. As a solution, several methods have been presented to reduce the time complexity of these polynomials in recent years. The purpose of this research is to study several methods among the most popular recursive methods for fast Zernike computation and compare them together by a global theoretical evaluation system called worst-case time complexity. In this study, we have analyzed the selected algorithms and calculated the worst-case time complexity for each one. After that, the results are represented and explained and finally, a conclusion has been made by comparing these criteria among the studied algorithms. According to time complexity, we have observed that although some algorithms such as Wee method and Modified Prata method were successful in having the smaller time complexities, some other approaches did not make any significant difference compared to the classical algorithm.

关键词： Time Complexity Uniform Model Zernike Moments Zernike Polynomi-als

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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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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

arXiv

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arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

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Fast Generalized Chan-Vese Model for plant/soil segmentation to estimate percentage of ground cover in agricultural images

Fast Generalized Chan-Vese Model for plant/soil segmentation...

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IEEE International Symposium on Signal processing and Information Technology (ISSPIT)

作者： Y. Boutiche A. Abdessalem N. Ramou N. Chetih Image and Signal Processing Laboratory Research Center in Industrial Technologies CRTI ex CSC Algiers Algeria College of science Sultan Qaboos University Muscat Oman

ISBN: (数字)9781728153414

ISBN: (纸本)9781728153421

Computer vision systems have been widely used in agriculture to perform tasks requiring segmentation and classification. In this paper, we describe a fast multi-channel implicit active contour method for performing plant/soil segmentation in color agriculture images. First, the Chan-Vese model is generalized to segment images in L* a* b* color space, then a level set method, capable of dealing with topology changes, is used to segment the whole image. Finally, a functional is optimized via a “sweeping” algorithm for fast convergence. Percentage of Ground Cover (PGC) is then easily estimated from the segmented image. Several experiments have been conducted to validate the proposed algorithm.

关键词： image segmentation image color analysis Agriculture Active contours Soil Level set Convergence

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Corrigendum to ' Optical Coherence Tomography in Neuromyelitis Optica spectrum disorder and Multiple Sclerosis: A population-based study' [Multiple Sclerosis and Related Disorders Volume 47 (2021) 1-8/ 102625]

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Multiple sclerosis and related disorders 2021年 54卷 103116页

作者： Fereshteh Ashtari Akram Ataei Raheleh Kafieh Zahra Khodabandeh Mahdi Barzegar Marzieh Raei Alireza Dehghani Marjan Mansurian Isfahan neurosciences research center Isfahan University of Medical Sciences Isfahan Iran. School of Advanced Technologies in Medicine Medical Image & Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Electronic address: rkafieh@***. School of Advanced Technologies in Medicine Medical Image & Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Department of Ophthalmology Isfahan University of Medical Sciences Isfahan Iran. Department of Epidemiology & Biostatistics School of Health Isfahan University of Medical Sciences Isfahan Iran.

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A hybrid fusion approach combining salience perception and generative adversarial networks for multimodal medical image integration

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Biomedical Signal processing and Control 2025年 110卷

作者： Fangfang Ye Jinming Wang Ting Wang Congcong Liu Salwa Othmen Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province College of Information Science and Technology Zhejiang Shuren University Hangzhou 310015 P.R. China Image Processing Research Center Zhejiang Shuren University Hangzhou 310015 P.R. China College of Information Science and Technology Nanjing Forestry University Nanjing 210037 P.R. China School of Information Engineering Huzhou University Huzhou 313000 P. R. China Department of Computers and Information Technologies College of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia

Multimodal medical image fusion using precise features improves disease diagnosis efficiency through clinical applications. Selecting optimal features is a significant task to match the decomposed representation of different imaging orientations to enhance clinical diagnosis precision. In this article, a hybrid fusion method (HFM) using Salience Perception and Generative Adversarial Network is introduced to reduce orientation errors in disease diagnosis. This method is designed exclusively for clinical images that require different orientations for identifying defects such as tumors, clots, etc. The salience perception method identifies the unbiased features with either of the categories based on grayscale pixel representations. The decomposed features are separately identified to prevent their impact on precision such that the bias range of the pixel distribution is balanced. The GAN concurrently operates on the biased and unbiased feature based on the separated distribution over the maximum size of the images. The GAN thus identifies the most unbiased features to achieve maximum orientation detection. Combining different features from multiple images relies on unbiased over biased features irrespective of the grayscale conversions to provide better precision. The two distinct layers of the GAN are trained using decomposed and un-biased features to reduce orientation errors. The proposed method achieves 15.6 % high precision for brain CT images and 16.2 % high precision for fundus image fusion with 11.7 % less orientation error for brain CT images and 11.65 % less orientation error for fundus images for the maximum features extracted.

关键词： GAN image fusion Medical image Orientation error Salience perception

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Livelayer: A semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images

arXiv

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arXiv 2020年

作者： Montazerin, Mansooreh Sajjadifar, Zahra Pour, Elias Khalili Riazi-Esfahani, Hamid Mahmoudi, Tahereh Rabbani, Hossein Movahedian, Hossein Dehghani, Alireza Akhlaghi, Mohammadreza Kafieh, Rahele Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Retina Service Farabi Eye Hospital Tehran University of Medical Sciences Tehran Iran Department of Biomedical Systems and Medical Physics Tehran University of Medical Sciences Tehran Iran Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Ophthalmology Isfahan University of Medical Sciences Isfahan Iran

Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called "Livelayer" which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra's Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15.57s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and © 2020, CC BY.

关键词： Optical tomography

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