scholarly journals The use of image analysis software increases the accuracy of the periacetabular osteotomy fragment placement

2021 ◽  
Author(s):  
Alison J Dittmer Flemig ◽  
Anthony Essilfie ◽  
Brandon Schneider ◽  
Stacy Robustelli ◽  
Ernest L Sink
Keyword(s):  
Image Analysis ◽  
Periacetabular Osteotomy ◽  
Target Range ◽  
Control Group ◽  
Study Group ◽  
Analysis Tool ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Acetabular Fragment ◽  
Center Edge

ABSTRACT The purpose of this study was to report on the use of image analysis technology to enhance accuracy of intra-operative imaging and evaluation of periacetabular osteotomy (PAO) correction. This was a retrospective study reporting on the first 25 cases of PAO performed with the use of an image analysis tool. This technology was used intra-operatively to assess the position of the supine coronal image in comparison to pre-operative standing images using a ratio of pelvic tilt (PT). Intra-operative PT, Tönnis angle, lateral center–edge angle (LCEA) and anterior wall index were compared to post-operative images. Post-operative radiographic parameters in the study group were compared with a control group of PAO cases performed prior to the implementation of the new software. The image analysis software was able to obtain intra-operative supine imaging that was equivalent to pre-operative standing imaging. When comparing the PAOs performed with the use of the software versus those without, the study group trended toward being more likely within the surgeon’s defined target range of radiographic values, which was statistically significant for LCEA. This tool can be used to assure the surgeon that the intra-operative image being used for surgical decision-making is representative of the functional radiograph. PAOs performed with the use of this technology showed enhanced accuracy of surgical correction for the parameters within our defined target ranges. This may increase the ability of the surgeon to place the acetabular fragment more precisely within his or her goal parameters for acetabular reorientation correction.

Quantitative Analysis of Alpha Morphology Changes of Ti-17 Alloy with a Lamellar Starting Microstructure

2009 ◽  
Vol 618-619 ◽  
pp. 113-116
Author(s):  
Wei Dong Zeng ◽  
Yi Gang Zhou ◽  
Jian Hua Zhou ◽  
Xiao Ying Wang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Image Analysis ◽  
Distribution Curve ◽  
Lamellar Microstructure ◽  
Analysis Software ◽  
Element Analysis ◽  
Image Analysis Software ◽  
Center Edge ◽  
Morphology Changes

The microstructural changes of alpha morphology in Ti-17 alloy with an initial lamellar microstructure were examined quantitatively using image analysis software. The alpha morphology changes at different locations (center, edge, 0.5R) of the cakes deformed to different strain level were measured by Feret ratio and related to the imposed strain estimated using finite element analysis. It was found that the modification of alpha morphology depended strongly on the forging strains and the locations in the cakes. The distribution curve of Feret ratio was characterized by a single peak at Feret ratio between 1.5-2.5. These peak values increased with the increase in the height reductions. Increasing forging strains caused the high-Feret-ratio lamellae to decrease but low-Feret-ratio alpha to increase noticeably. The average strains for initiation of globalization were of the order of 0.4 and those for completion were of the order of 1.0, which was lower than conventional α+β titanium alloys.

Products/Services Highlights

2003 ◽  
Vol 07 (22) ◽  
pp. 1407-1416
Keyword(s):  
Image Analysis ◽  
Microarray Analysis ◽  
High Performance ◽  
Analysis Tool ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Cancer Diagnostic ◽  
Diagnostic Facility

Latest Cancer Diagnostic Facility in Japan. Rockeby Launches World's First 5-Minute Fungal Test. Bacterial BarCodes' DiversiLab System Leverages on Agilent's 2100 Bioanalyzer. Amersham Biosciences Introduces New High-performance Image Analysis Software. Ocimum Biosolutions' Unique Genowiz™ Microarray Analysis Tool.

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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