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Yang R, Chen L, Zhang L, Li Z, Lin Y, Wu Y. Image Enhancement via Special Functions and Its Application for Near Infrared Imaging. GLOBAL CHALLENGES (HOBOKEN, NJ) 2023; 7:2200179. [PMID: 37483414 PMCID: PMC10362124 DOI: 10.1002/gch2.202200179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/26/2023] [Indexed: 07/25/2023]
Abstract
Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging.
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Affiliation(s)
- Ruoxi Yang
- School of Electrical and Automation EngineeringNanjing Normal UniversityNanjing210046China
| | - Long Chen
- School of Electrical and Automation EngineeringNanjing Normal UniversityNanjing210046China
| | - Ling Zhang
- College of Microelectronics and Communication EngineeringChongqing UniversityChongqing400044China
| | - Zongan Li
- School of Electrical and Automation EngineeringNanjing Normal UniversityNanjing210046China
| | - Yingcheng Lin
- College of Microelectronics and Communication EngineeringChongqing UniversityChongqing400044China
| | - Ye Wu
- School of Electrical and Automation EngineeringNanjing Normal UniversityNanjing210046China
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Teranikar T, Villarreal C, Salehin N, Ijaseun T, Lim J, Dominguez C, Nguyen V, Cao H, Chuong C, Lee J. SCALE SPACE DETECTOR FOR ANALYZING SPATIOTEMPORAL VENTRICULAR CONTRACTILITY AND NUCLEAR MORPHOGENESIS IN ZEBRAFISH. iScience 2022; 25:104876. [PMID: 36034231 PMCID: PMC9404658 DOI: 10.1016/j.isci.2022.104876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 04/01/2022] [Accepted: 07/29/2022] [Indexed: 11/15/2022] Open
Abstract
In vivo quantitative assessment of structural and functional biomarkers is essential for characterizing the pathophysiology of congenital disorders. In this regard, fixed tissue analysis has offered revolutionary insights into the underlying cellular architecture. However, histological analysis faces major drawbacks with respect to lack of spatiotemporal sampling and tissue artifacts during sample preparation. This study demonstrates the potential of light sheet fluorescence microscopy (LSFM) as a non-invasive, 4D (3days + time) optical sectioning tool for revealing cardiac mechano-transduction in zebrafish. Furthermore, we have described the utility of a scale and size-invariant feature detector, for analyzing individual morphology of fused cardiomyocyte nuclei and characterizing zebrafish ventricular contractility. Cardiac defect genes in humans have corresponding zebrafish orthologs Light sheet modality is very effective for non-invasive, 4D modeling of zebrafish Hessian detector is robust to varying nuclei scales and geometric transformations Watershed filter is effective for separating fused cellular volumes
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Affiliation(s)
- Tanveer Teranikar
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Cameron Villarreal
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Nabid Salehin
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Toluwani Ijaseun
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Jessica Lim
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Cynthia Dominguez
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Vivian Nguyen
- Martin High School/ UT Arlington, Arlington, TX, USA
| | - Hung Cao
- Department of Electrical Engineering, UC Irvine, Irvine, CA, USA
| | - Cheng–Jen Chuong
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Juhyun Lee
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
- Department of Medical Education, TCU and UNTHSC School of Medicine, Fort Worth, TX 76107, USA
- Corresponding author
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Qiao S, Yu Q, Zhao Z, Song L, Tao H, Zhang T, Zhao C. Edge extraction method for medical images based on improved local binary pattern combined with edge-aware filtering. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Chetia R, Sahu PP. Quantum Edge Extraction of Chest CT Image for the Detection and Differentiation of Infected Region of COVID-19 Patient. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022; 48:1-12. [PMID: 35127328 PMCID: PMC8800831 DOI: 10.1007/s13369-021-06511-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 10/05/2021] [Indexed: 11/25/2022]
Abstract
The COVID-19 outbreak requires urgent public health attention throughout the world due to having its fast human to human transmission. As per the guidelines of the World Health Organization, rapid testing, vaccination, and isolation are the only options to break the chain of COVID-19 infection. Lung computed tomography (CT) plays a prime role in the accurate detection of COVID-19. For detection and pattern analysis of COVID-19, here an improved Sobel quantum edge extraction with non-maximum suppression and adaptive threshold (ISQEENSAT) has been employed to extract clinical information of infected lungs suppressing minimal noises present in the chest. In comparison with conventional classical edge extraction operators, the proposed technique can detect more sharp and accurate clinical edges of peripheral ground-glass opacity that appeared in the initial stage of COVID-19 patients. The edge extraction results assure the detection and differentiation of COVID-19 infection. ISQEENSAT can be a useful tool for assisting COVID-19 analysis and can help the physician to detect the region how much it has infected. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13369-021-06511-9.
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Affiliation(s)
- Rajib Chetia
- Department of ECE, Tezpur University, Tezpur, Napam, Assam 784028 India
- Department of ECE, Central Institute of Technology (CIT), Kokrajhar, BTAD, Assam 783370 India
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