Optimization of cDNA microarray image analysis methods

2009 ◽  
Author(s):  
Antonios Daskalakis
Keyword(s):  
Image Analysis ◽  
Cdna Microarray ◽  
Microarray Image ◽  
Analysis Methods

Segmentation of cDNA Microarray Image Using Fuzzy c-Mean Algorithm and Mathematical Morphology

2011 ◽  
Vol 464 ◽  
pp. 159-162 ◽  
Author(s):  
Zhi Yao Li ◽  
Gui Rong Weng
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Cdna Microarray ◽  
Glass Slide ◽  
Sequence Information ◽  
Microarray Technology ◽  
Segmentation Method ◽  
Microarray Image ◽  
Adaptive Partitioning ◽  
Low Expression

cDNA microarray technology provides an effectual tool to explore the enormous genome. cDNA microarray consists of thousands of gene sequences which are printed on glass slide and these sequence information can be obtained by forming a microarray image. So image analysis is crucial. However, image segmentation is another key point. How to deal with the gene spots which are always comprised with imperfection such as irregular contours, donut shapes, artifact and spots with low expression is important to the robustness of the segmentation method. The paper proposed a method based on fuzzy c-mean algorithm which can effectively avoid the influence of various types of artifacts through adaptive partitioning.

scholarly journals Detecting Apples in the Wild: Potential for Harvest Quantity Estimation

2021 ◽  
Vol 13 (14) ◽  
pp. 8054
Author(s):  
Artur Janowski ◽  
Rafał Kaźmierczak ◽  
Cezary Kowalczyk ◽  
Jakub Szulwic
Keyword(s):  
Image Analysis ◽  
Pilot Study ◽  
Production Management ◽  
Satellite System ◽  
Morphological Operations ◽  
Computational Performance ◽  
Analysis Methods ◽  
Manual Counting ◽  
In The Wild ◽  
Global Navigation Satellite

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.

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