scholarly journals Low-cost, portable optical imaging systems for cancer diagnosis

Author(s):  
M C Pierce ◽  
Rebecca Richards-Kortum
Author(s):  
Marcelline E. Dechenaud ◽  
Samantha Kennedy ◽  
Sima Sobhiyeh ◽  
John Shepherd ◽  
Steven B. Heymsfield

2021 ◽  
Vol 50 (1) ◽  
pp. 123-130
Author(s):  
赵珩翔 Hengxiang ZHAO ◽  
李立波 Libo LI ◽  
冯玉涛 Yutao FENG ◽  
李勇 Yong LI ◽  
刘薇 Wei LIU ◽  
...  

2021 ◽  
Author(s):  
Michał Meina ◽  
Patrycjusz Stremplewski ◽  
Carlos Lopez-Mariscal ◽  
Szymon Tamborski ◽  
Maciej Bartuzel ◽  
...  

2021 ◽  
Vol 3 (04) ◽  
Author(s):  
Jongchan Park ◽  
David J. Brady ◽  
Guoan Zheng ◽  
Lei Tian ◽  
Liang Gao

Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises


Nanophotonics ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 713-730 ◽  
Author(s):  
Fulya Ekiz-Kanik ◽  
Derin Deniz Sevenler ◽  
Neşe Lortlar Ünlü ◽  
Marcella Chiari ◽  
M. Selim Ünlü

AbstractBiological nanoparticles such as viruses and exosomes are important biomarkers for a range of medical conditions, from infectious diseases to cancer. Biological sensors that detect whole viruses and exosomes with high specificity, yet without additional labeling, are promising because they reduce the complexity of sample preparation and may improve measurement quality by retaining information about nanoscale physical structure of the bio-nanoparticle (BNP). Towards this end, a variety of BNP biosensor technologies have been developed, several of which are capable of enumerating the precise number of detected viruses or exosomes and analyzing physical properties of each individual particle. Optical imaging techniques are promising candidates among broad range of label-free nanoparticle detectors. These imaging BNP sensors detect the binding of single nanoparticles on a flat surface functionalized with a specific capture molecule or an array of multiplexed capture probes. The functionalization step confers all molecular specificity for the sensor’s target but can introduce an unforeseen problem; a rough and inhomogeneous surface coating can be a source of noise, as these sensors detect small local changes in optical refractive index. In this paper, we review several optical technologies for label-free BNP detectors with a focus on imaging systems. We compare the surface-imaging methods including dark-field, surface plasmon resonance imaging and interference reflectance imaging. We discuss the importance of ensuring consistently uniform and smooth surface coatings of capture molecules for these types of biosensors and finally summarize several methods that have been developed towards addressing this challenge.


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