scholarly journals Development of an image processing MATLAB algorithm for cell growth analysis

2019 ◽  
Author(s):  
Guilherme Antonio Dutra Morais ◽  
Eric Fujiwara ◽  
Marco César Prado Soares ◽  
Lucimara G. de la Torre
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Cell Growth ◽  
Graphical User Interface ◽  
Growth Analysis ◽  
Image Processing Algorithm ◽  
Reliability Of Results ◽  
Automatic Counting ◽  
Microscope Images ◽  
Fluorescent Cells

An image processing algorithm was developed using a MATLAB Graphical User Interface (GUI), allowing the automatic counting and analysis of fluorescent cells in microscope images. This software improves the reliability of results obtained by low-experienced researchers and can be used as feedback for maintaining the correct environmental conditions for fermentation.

Efficient Rebar Count Scheme Based on Image Recognition in Mechanical Engineering

2013 ◽  
Vol 340 ◽  
pp. 805-808
Author(s):  
Yi Long Lei ◽  
Jiong Zhao Yang ◽  
Yu Huan Zhang
Keyword(s):  
Image Processing ◽  
Image Recognition ◽  
Enterprise Management ◽  
Labor Intensity ◽  
Root Number ◽  
Image Processing Algorithm ◽  
Counting System ◽  
Automatic Counting ◽  
Steel Bar ◽  
System Composition

Nowadays, along with the higher requirement of the customer and the standardization of enterprise management, the finished product of steel bar production must be standardization packaged by root number; management requirements of bar fixed bundle of sticks are more stringent. The artificial count is used into the most of the steel bar production recently. So there are many problems. Such as labor intensity, easy fatigue, less efficient, and error-prone. The image recognition technology for online automatic counting system for a given period of time. It also can improve the speed and make the product more accuracy. This paper mainly talks about the system composition and image processing algorithm.

A graphical user-interface and an image processing toolkit for Perl

2005 ◽  
Vol 35 (5) ◽  
pp. 467-490
Author(s):  
Dmitry Karasik ◽  
Anton Berezin ◽  
Vadim Belman ◽  
Jon Sporring
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Graphical User Interface

Image transfer and computer-supported cooperative diagnosis

1997 ◽  
Vol 3 (2) ◽  
pp. 103-107 ◽  
Author(s):  
H Handels ◽  
E Rinast ◽  
Ch Busch ◽  
Ch Hahn ◽  
V Kuhn ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Graphical User Interface ◽  
Medical Image ◽  
Low Cost ◽  
User Interaction ◽  
Magnetic Resonance Images ◽  
Computer Supported Cooperative Work ◽  
Communication Tool ◽  
Image Transfer

The KAMEDIN system was designed as a low-cost communication tool as part of a computer-supported cooperative work project that included synchronized user interaction, telepointing and audioconferencing. During a five-month field trial, it was used for medical image transfer and cooperative diagnosis in 14 clinics and medical departments in Germany. During the field test, 297 teleconsultations were performed via ISDN and 875 MByte of data were transferred. An image compression ratio of 2-3 was obtained, so that the total quantity of data transferred corresponded to 14,000-21,000 magnetic resonance images or 3500-5250 computerized tomography images. Furthermore, 694 local sessions were conducted for the preparation of teleconsultations and the review of transferred images. Participants learned to handle the KAMEDIN system in a few hours. This was mainly owing to the design of the user-oriented graphical user interface and the restriction of the system to a set of essential image-processing functions.

scholarly journals Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

2012 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
PZ Nadila ◽  
YHP Manurung ◽  
SA Halim ◽  
SK Abas ◽  
G Tham ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Image Quality ◽  
Digital Radiography ◽  
Graphical User Interface ◽  
Signal To Noise Ratio ◽  
Quality Measurement ◽  
Noise Removal ◽  
Signal To Noise ◽  
Noise Ratio

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB. 

scholarly journals Histological Image Processing for the Assessment of Tissue Engineered Cartilage

2018 ◽  
Vol 4 (1) ◽  
pp. 461-464
Author(s):  
Michael Munz ◽  
Fabian Och ◽  
Ulla Wenzel ◽  
Martin Hessling ◽  
Katja Hasch ◽  
...  
Keyword(s):  
Image Processing ◽  
Cell Nuclei ◽  
Collagen Scaffolds ◽  
Image Processing Algorithm ◽  
Histological Image ◽  
White Balance ◽  
Microscope Images ◽  
Human Nasal Septum ◽  
Engineered Cartilage ◽  
Porcine Collagen

AbstractIn a recently developed perfusion bioreactor, human nasal septum chondrocytes were seeded on decellularized porcine collagen scaffolds and cultivated for 6 weeks. Afterwards, the samples were stained with alcian blue and multiple histological microscope images were taken to assess the migration of the human cells and their redifferentiation. In this paper, we propose an image processing algorithm to objectively quantify the cultivation results. This algorithm performs a white balance, detects cell nuclei and their migration depth, and detects lacunae and their orientation. To evaluate performance, sensitivity and precision are calculated using manually labeled data over 50 images. The detection of cell nuclei has a reasonable precision (73%), but the level of sensitivity (42%) should be further improved. The proposed method demonstrates acceptable level of precision (83%) and sensitivity (86%) for the detection of lacunae.

Sign in / Sign up

Export Citation Format

Share Document