Image Analysis for cDNA Microarrays

2005 ◽  
Vol 44 (03) ◽  
pp. 405-407 ◽  
Author(s):  
J. Rahnenführer
Keyword(s):  
Image Analysis ◽  
Information Loss ◽  
Intensity Histogram ◽  
Background Estimation ◽  
Detection Algorithms ◽  
Microarray Image ◽  
Hybrid Approaches ◽  
Algorithmic Approaches ◽  
Array Hybridization

Summary Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. Methods: We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections. Results: For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection algorithms, intensity histogram methods and hybrid approaches have been proposed. The correctness of final intensity estimates is difficult to verify. Nevertheless, the application of sophisticated algorithms provides a significant reduction of the possible information loss. Conclusions: The initial analysis step for array hybridization experiments is the estimation of expression intensities. The quality of this process is crucial for the validity of interpretations from subsequent analysis steps.

scholarly journals Microarray spot partitioning by autonoumsly organising maps thorugh contour model

2020 ◽  
Vol 10 (1) ◽  
pp. 746 ◽  
Author(s):  
Karthik S. A. ◽  
Manjunath S. S.
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Experimental Work ◽  
Microarray Image ◽  
Test Spot ◽  
Contour Model ◽  
Comprehensive Information ◽  
Microarray Spot

In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization.

An image analysis based expert system for assessing the quality of freeze-dried protein formulations

2012 ◽  
Author(s):  
Jukka Rantanen ◽  
Hjalte Trnka ◽  
Jian Wu ◽  
Marco van de Weert ◽  
Holger Grohganz
Keyword(s):  
Image Analysis ◽  
Expert System ◽  
Protein Formulations ◽  
Freeze Dried

scholarly journals Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

2021 ◽  
Vol 733 (1) ◽  
pp. 012005
Author(s):  
Y Hendrawan ◽  
R Utami ◽  
D Y Nurseta ◽  
Daisy ◽  
S Nuryani ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chili Pepper ◽  
Capsicum Frutescens

scholarly journals Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 307
Author(s):  
Dawid Wojcieszak ◽  
Maciej Zaborowicz ◽  
Jacek Przybył ◽  
Piotr Boniecki ◽  
Aleksander Jędruś
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Dry Matter ◽  
Neural Model ◽  
Original Method ◽  
Neural Models ◽  
Test Error ◽  
Effective Assessment ◽  
Modelling Methods

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

scholarly journals Image data of crumb structure of bread from flour of czech spring wheat cultivars

2011 ◽  
Vol 22 (No. 4) ◽  
pp. 133-142 ◽  
Author(s):  
I. Švec ◽  
M. Hrušková
Keyword(s):  
Image Analysis ◽  
Image Data ◽  
Analysis Data ◽  
Wheat Cultivars ◽  
Rheological Measurements ◽  
Class A ◽  
Bread Volume ◽  
Class B ◽  
Crumb Structure

Abstract: Baking quality of flour from six wheat cultivars (harvest 2002 and 2003), belonging to the quality classes A and B, was evaluated using the fermented dough test. Analytical traits of kernel and flour showed differences between the classes which were confirmed by the baking test with the full-bread-formula according to Czech method. In addition to standard methods of the bread parameters description (specific bread volume and bread shape measurements) rheological measurements of penetrometer and image analysis were used in effort to differentiate wheat samples into the quality classes. The results of the baking test proved significant differences in specific bread volumes – the highest volume in class A was obtained with the cultivar Vinjet and in class B with SG-S1098 – approx. 410 and 420 ml/100 g. Although significant correlations among image analysis data and specific bread volume having been proved, any image analysis parameter did not distinguish the quality classes. Only the penetronetric measurements made with bread crumb were suitable for such purpose (r = 0.9083; for  = 0.01). Among image analysis data the total cell area of the crumb had the strongest correlation with specific bread volume (r = 0.7840; for α = 0.01).    

scholarly journals A smartphone application to measure the quality of pest control spraying machines via image analysis

2018 ◽  
Author(s):  
Bruno Brandoli Machado ◽  
Gabriel Spadon ◽  
Mauro S. Arruda ◽  
Wesley N. Goncalves ◽  
Andre C. P. L. F. Carvalho ◽  
...  
Keyword(s):  
Image Analysis ◽  
Pest Control ◽  
Smartphone Application

scholarly journals Automated image analysis for array hybridization experiments

2001 ◽  
Vol 17 (7) ◽  
pp. 634-641 ◽  
Author(s):  
M. Steinfath ◽  
W. Wruck ◽  
H. Seidel ◽  
H. Lehrach ◽  
U. Radelof ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Array Hybridization

scholarly journals On-the-Go Grapevine Yield Estimation Using Image Analysis and Boolean Model

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Borja Millan ◽  
Santiago Velasco-Forero ◽  
Arturo Aquino ◽  
Javier Tardaguila
Keyword(s):  
Image Analysis ◽  
Boolean Model ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Yield Estimation ◽  
Model Image ◽  
Mean Error ◽  
Cluster Area ◽  
The Relationship

This paper describes a new methodology for noninvasive, objective, and automated assessment of yield in vineyards using image analysis and Boolean model. Image analysis, as an inexpensive and noninvasive procedure, has been studied for this purpose, but the effect of occlusions from the cluster or other organs of the vine has an impact that diminishes the quality of the results. To reduce the influence of the occlusions in the estimation, the number of berries was assessed using the Boolean model. To evaluate the methodology, three different datasets were studied: cluster images, manually acquired vine images, and vine images captured on-the-go using a quad. The proposed algorithm estimated the number of berries in cluster images with a root mean square error (RMSE) of 20 and a coefficient of determination (R2) of 0.80. Vine images manually taken were evaluated, providing 310 grams of mean error and R2=0.81. Finally, images captured using a quad equipped with artificial light and automatic camera triggering were also analysed. The estimation obtained applying the Boolean model had 610 grams of mean error per segment (three vines) and R2=0.78. The reliability against occlusions and segmentation errors of the Boolean model makes it ideal for vineyard yield estimation. Its application greatly improved the results when compared to a simpler estimator based on the relationship between cluster area and weight.

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