scholarly journals SpotMetrics: An Open-Source Image-Analysis Software Plugin for Automatic Chromatophore Detection and Measurement

2017 ◽  
Vol 8 ◽  
Author(s):  
Stavros P. Hadjisolomou ◽  
George El-Haddad
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Source Image ◽  
Analysis Software ◽  
Image Analysis Software

scholarly journals ChromaWizard: An open source image analysis software for multicolor fluorescence in situ hybridization analysis

2018 ◽  
Vol 93 (7) ◽  
pp. 749-754
Author(s):  
Norbert Auer ◽  
Astrid Hrdina ◽  
Chaitra Hiremath ◽  
Sabine Vcelar ◽  
Martina Baumann ◽  
...  
Keyword(s):  
Image Analysis ◽  
In Situ Hybridization ◽  
Fluorescence In Situ Hybridization ◽  
Open Source ◽  
Hybridization Analysis ◽  
Source Image ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Multicolor Fluorescence

Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software

2017 ◽  
Vol 472 (2) ◽  
pp. 259-269 ◽  
Author(s):  
Andres Moon ◽  
Geoffrey H. Smith ◽  
Jun Kong ◽  
Thomas E. Rogers ◽  
Carla L. Ellis ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Renal Allograft ◽  
Source Image ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Allograft Biopsy

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
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