scholarly journals Correction to: Environmental, scanning electron and optical microscope image analysis software for determining volume and occupied area of solid-state fermentation fungal cultures

2011 ◽  
Vol 6 (5) ◽  
pp. 609-609
Author(s):  
Johann F. Osma ◽  
José L. Toca-Herrera ◽  
Susana Rodríguez-Couto
Keyword(s):  
Image Analysis ◽  
Solid State ◽  
Solid State Fermentation ◽  
Optical Microscope ◽  
Environmental Scanning ◽  
Analysis Software ◽  
Microscope Image ◽  
Image Analysis Software ◽  
Optical Microscope Image ◽  
Scanning Electron

Ciliary count in chronic suppurative otitis media: comparative quantitative study between mucosal and squamous types using scanning electron microscopy and image analysis

2004 ◽  
Vol 118 (5) ◽  
pp. 343-347 ◽  
Author(s):  
Ahmed Atef ◽  
Essam Ezat Ayad
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Image Analysis ◽  
Otitis Media ◽  
Quantitative Study ◽  
Chronic Suppurative Otitis Media ◽  
Analysis Software ◽  
Suppurative Otitis Media ◽  
Image Analysis Software ◽  
Scanning Electron

The objective of this study was to prove ciliary destruction in the middle-ear mucous membrane in cases of chronic suppurative otitis media (CSOM) and to compare both types of chronic suppurative otitis media with regard to the degree of ciliary destruction and ciliary count using objective quantitative techniques. The mucosa of the anterior mesotympanum over the promontory was sampled in 10 patients with mucosal CSOM and in another 10 patients with squamous type CSOM. Specimens were examined by scanning electron microscopy in combination with image analysis software techniques in order to study the cilia under higher magnifications and to calculate the ciliary area. Five patients with otosclerosis, no history suggestive of otitis media and normal ear drum appearance served as controls. Samples were taken and studied at the Faculty of Medicine of Cairo University. CSOM was found to be associated with significant ciliary destruction and this was more evident in the squamous type than in the mucosal type.

scholarly journals Influence of the presence of Cacoxenus indagator Loew. parasite larvae in brood chambers on the emergence rate and size of red mason bees

2016 ◽  
Vol 72 (9) ◽  
pp. 567-570
Author(s):  
Barbara Zajdel ◽  
Monika Fliszkiewicz ◽  
Kornelia Kucharska ◽  
Jakub Gąbka
Keyword(s):  
Image Analysis ◽  
Control Group ◽  
Nest Material ◽  
Analysis Software ◽  
Brood Chamber ◽  
Artificial Nests ◽  
Osmia Bicornis ◽  
Microscope Image ◽  
Image Analysis Software ◽  
Emergence Rate

Cacoxenus indagator is one of cleptoparasites most frequently found in the nests of Osmia bicornis L. The goal of this experiment was to examine the influence of the presence of 2-3 C. indagator larvae in the brood chamber on the cocoon mass, on the mass and size of bee imagines, and on their emergence rate. During the analysis of red mason bee nest material, 200 cocoons were taken from brood chambers, each of them also containing 2-3 larvae of C. indagator (CC). The control group consisted of 200 randomly chosen cocoons from brood chambers with no parasites inside (CFFC). The cocoons and the emerged bees were weighed, and then the size of the bees was determined by the microscope image analysis software Axio Vision Rel. 4.0 coupled with a Stereo Lumar V12 stereoscopic microscope (Carl Zeiss, Germany). This involved measuring the sum of the widths of tergites 3 and 4, the distance between the wings and the forewing length and width. It was found that the presence of 2-3 C. indagator larvae in the brood chamber had no impact on the mortality of bees in cocoons. The research demonstrates that CC cocoons do not have to be removed when collecting cocoons from artificial nests in managed O. bicornis populations, as bees emerging from such cocoons are fully developed.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Image analysis software and sample preparation demands

1990 ◽  
Author(s):  
Karl n. Roth ◽  
Knut Wenzelides ◽  
Guenter Wolf ◽  
Peter Hufnagl
Keyword(s):  
Image Analysis ◽  
Sample Preparation ◽  
Analysis Software ◽  
Image Analysis Software

Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

2016 ◽  
Vol 56 (12) ◽  
pp. 2060 ◽  
Author(s):  
Serkan Ozkaya ◽  
Wojciech Neja ◽  
Sylwia Krezel-Czopek ◽  
Adam Oler
Keyword(s):  
Image Analysis ◽  
Body Length ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Digital Camera ◽  
Body Measurements ◽  
Analysis Software ◽  
Body Area ◽  
Image Analysis Software

The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

Research on image analysis software for elevator brake wheel monitoring

2021 ◽  
Author(s):  
Shuangchang Feng ◽  
Pengzhao Zhang ◽  
Wenhao Shen ◽  
Pengbo Liu
Keyword(s):  
Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software
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