scholarly journals A simple computer vision algorithm as a clinical aid for the pathologist

2019 ◽  
Author(s):  
J.M. Lázaro-Guevara ◽  
B.J. Flores-Robles ◽  
A.E. Murga ◽  
K.M. Garrido
Keyword(s):  
Computer Vision ◽  
Screening Method ◽  
Clinical Work ◽  
Simple Computer ◽  
Risk Of Malignancy ◽  
Cell Shapes ◽  
High Resolution Images ◽  
Clinical Meaning ◽  
Increasing Demand ◽  
Vision Algorithm

AbstractHistological analysis for cancer detection or stratification is performed by observing and examining a small portion of a biopsied tissue under a microscope. Nevertheless, to assign clinical meaning to the findings, the analysis and interpretation of an experienced Pathologist is always necessary. Using high-resolution images, these experts visually examine the sample looking for specific characteristics on the cell shapes and tissue distributions, so they could decide whether tissue regions are cancerous, and establish the malignancy level of it. However, with the increasing demand for work for those pathologists and the importance of accuracy on diagnostics, multiple attempts to simplify their work have been performed. Current Imaging technologies allow novel horizons in the automatized selection of some of the characteristics that indicate malignancy in a biopsy. In this work, we propose a simple computer vision algorithm that can be implemented as a screening method for focusing in histological areas with higher risk of malignancy saving time to the pathologist and helping to perform a more standardized work, an easy observation with the potential to become in an aid to daily clinical work.

Simple Computer Vision Algorithm Production using OpenCV for “Virtual Ecosystem” project.

2020 ◽  
Vol 7 (1) ◽  
pp. 23-27
Author(s):  
Andrew Willis ◽  
Kemal Hasan
Keyword(s):  
Computer Vision ◽  
Digital Image ◽  
White Background ◽  
Qr Code ◽  
Process Information ◽  
Simple Computer ◽  
Vision Algorithm ◽  
Virtual Ecosystem

The main purposes of this research is to produce aComputer Vision (CV) algorithm using various library in pythonprogramming language (mainly OpenCV, Numpy, and ZBar) toautomate extraction and process information from an analogdrawing into a digital image to be used in the “Virtual Ecosystem”project. The Computer Vision step will include detecting datawithin the analog drawing using QR-code, determine drawingarea, replace white background with certain threshold withtransparency, and finally save the digital image following requiredratio.

scholarly journals Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)

2022 ◽  
Vol 2161 (1) ◽  
pp. 012059
Author(s):  
Rohan Nigam ◽  
Meghana Rao ◽  
Nihal Rian Dias ◽  
Arjun Hariharan ◽  
Amit Choraria ◽  
...  
Keyword(s):  
Computer Vision ◽  
Primary Source ◽  
Plant Health ◽  
Critical Conditions ◽  
Factors Affecting ◽  
Crucial Information ◽  
Increasing Demand ◽  
Vision Algorithm ◽  
User Friendly ◽  
Monitoring Platform

Abstract Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.

Remarks on the application of backscattered electron imaging (BEI) to the scanning electron microscopy (SEM) of biological structures

1983 ◽  
Vol 41 ◽  
pp. 484-487
Author(s):  
Etienne de Harven
Keyword(s):  
High Resolution ◽  
Incident Beam ◽  
Spot Size ◽  
Primary Electron ◽  
Critical Point Drying ◽  
Cell Shapes ◽  
High Resolution Images ◽  
Backscattered Electron ◽  
Electron Imaging ◽  
Scanning Electron

Biological ultrastructures have been extensively studied with the scanning electron microscope (SEM) for the past 12 years mainly because this instrument offers accurate and reproducible high resolution images of cell shapes, provided the cells are dried in ways which will spare them the damage which would be caused by air drying. This can be achieved by several techniques among which the critical point drying technique of T. Anderson has been, by far, the most reproducibly successful. Many biologists, however, have been interpreting SEM micrographs in terms of an exclusive secondary electron imaging (SEI) process in which the resolution is primarily limited by the spot size of the primary incident beam. in fact, this is not the case since it appears that high resolution, even on uncoated samples, is probably compromised by the emission of secondary electrons of much more complex origin.When an incident primary electron beam interacts with the surface of most biological samples, a large percentage of the electrons penetrate below the surface of the exposed cells.

Measurement of cemented carbide-PCD microdrill geometry error based on Computer Vision algorithm

Measurement ◽  
2021 ◽  
pp. 110186
Author(s):  
Siti Nurfadilah Binti Jaini ◽  
Deug-Woo Lee ◽  
Kang-Seok Kim ◽  
Seung-Jun Lee
Keyword(s):  
Computer Vision ◽  
Cemented Carbide ◽  
Vision Algorithm ◽  
Geometry Error

Deep Learning and Computer Vision in Smart Agriculture

2020 ◽  
pp. 66-88
Author(s):  
Shiv Kumar ◽  
Agrima Yadav ◽  
Deepak Kumar Sharma
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Life Cycle ◽  
World Population ◽  
Traditional Methods ◽  
Demand And Supply ◽  
The World ◽  
Increasing Demand ◽  
Smart Agriculture ◽  
The Many

The exponential growth in the world population has led to an ever-increasing demand for food supplies. This has led to the realization that conventional and traditional methods alone might not be able to keep up with this demand. Smart agriculture is being regarded as one of the few realistic ways that, together with the traditional methods, can be used to close the gap between the demand and supply. Smart agriculture integrates the use of different technologies to better monitor, operate, and analyze different activities involved in different phases of the agricultural life cycle. Smart agriculture happens to be one of the many disciplines where deep learning and computer vision are being realized to be of major impact. This chapter gives a detailed explanation of different deep learning methods and tries to provide a basic understanding as to how these techniques are impacting different applications in smart agriculture.

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