Comparison of image analysis and stereologic techniques used to quantify endothelial cell and pericyte populations in the rat retinal microvessels following proton irradiation: Results of a pilot study

2000 ◽  
Vol 48 (3) ◽  
pp. 268-269
Author(s):  
J.O Archambeau ◽  
X Mao ◽  
R Grove
2021 ◽  
Vol 13 (14) ◽  
pp. 8054
Author(s):  
Artur Janowski ◽  
Rafał Kaźmierczak ◽  
Cezary Kowalczyk ◽  
Jakub Szulwic

Knowing the exact number of fruits and trees helps farmers to make better decisions in their orchard production management. The current practice of crop estimation practice often involves manual counting of fruits (before harvesting), which is an extremely time-consuming and costly process. Additionally, this is not practicable for large orchards. Thanks to the changes that have taken place in recent years in the field of image analysis methods and computational performance, it is possible to create solutions for automatic fruit counting based on registered digital images. The pilot study aims to confirm the state of knowledge in the use of three methods (You Only Look Once—YOLO, Viola–Jones—a method based on the synergy of morphological operations of digital imagesand Hough transformation) of image recognition for apple detecting and counting. The study compared the results of three image analysis methods that can be used for counting apple fruits. They were validated, and their results allowed the recommendation of a method based on the YOLO algorithm for the proposed solution. It was based on the use of mass accessible devices (smartphones equipped with a camera with the required accuracy of image acquisition and accurate Global Navigation Satellite System (GNSS) positioning) for orchard owners to count growing apples. In our pilot study, three methods of counting apples were tested to create an automatic system for estimating apple yields in orchards. The test orchard is located at the University of Warmia and Mazury in Olsztyn. The tests were carried out on four trees located in different parts of the orchard. For the tests used, the dataset contained 1102 apple images and 3800 background images without fruits.


2019 ◽  
Vol 8 (5) ◽  
pp. 412-418 ◽  
Author(s):  
Niek B. Achten ◽  
Matijs van Meurs ◽  
Rianne M. Jongman ◽  
Amadu Juliana ◽  
Grietje Molema ◽  
...  

2017 ◽  
Vol 18 ◽  
pp. 226-231 ◽  
Author(s):  
Monalisa Jacob Guiselini ◽  
Alessandro Melo Deana ◽  
Daniela de Fátima Teixeira da Silva ◽  
Nelson Hideyoshi Koshoji ◽  
Raquel Agnelli Mesquita-Ferrari ◽  
...  

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 923
Author(s):  
Bogdan Silviu Ungureanu ◽  
Daniel Pirici ◽  
Simona Olimpia Dima ◽  
Irinel Popescu ◽  
Gheorghe Hundorfean ◽  
...  

Ex-vivo freshly surgical removed pancreatic ductal adenocarcinoma (PDAC) specimens were assessed using pCLE and then processed for paraffin embeding and histopathological diagnostic in an endeavour to find putative image analysis algorithms that might recognise adenocarcinoma. Methods: Twelve patients diagnosed with PDAC on endoscopic ultrasound and FNA confirmation underwent surgery. Removed samples were sprayed with acriflavine as contrast agent, underwent pCLE with an experimental probe and compared with previous recordings of normal pancreatic tissue. Subsequently, all samples were subjected to cross-sectional histopathology, including surgical resection margins for controls. pCLE records, as well as corespondant cytokeratin-targeted immunohistochemistry images were processed using the same morphological classifiers in the Image ProPlus AMS image analysis software. Specific morphometric classifiers were automatically generated on all images: Area, Hole Area (HA), Perimeter, Roundness, Integrated Optical Density (IOD), Fractal Dimension (FD), Ferret max (Fmax), Ferret mean (Fmean), Heterogeneity and Clumpiness. Results: After histopathological confirmation of adenocarcinoma areas, we have found that the same morphological classifiers could clearly differentiate between tumor and non-tumor areas on both pathology and correspondand pCLE (area, roundness, IOD, ferret and heterogeneity (p < 0.001), perimeter and hole area (p < 0.05). Conclusions: This pilot study proves that classical morphometrical classifiers can clearly differentiate adenocarcimoma on pCLE data, and the implementation in a live image-analysis algorithm might help in improving the specificity of pCLE in vivo diagnostic.


2010 ◽  
Vol 13 (1-2) ◽  
pp. B54-B57 ◽  
Author(s):  
Bora Garipcan ◽  
Stefan Maenz ◽  
Tam Pham ◽  
Utz Settmacher ◽  
Klaus D. Jandt ◽  
...  

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