Fast automated cell phenotype image classification

Hamilton, N. A., Pantelic, R. S., Hanson, K. and Teasdale, R. D. (2007) Fast automated cell phenotype image classification. BMC Bioinformatics, 8 110: 1-8. doi:10.1186/1471-2105-8-110

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Author Hamilton, N. A.
Pantelic, R. S.
Hanson, K.
Teasdale, R. D.
Title Fast automated cell phenotype image classification
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2007
Sub-type Article (original research)
DOI 10.1186/1471-2105-8-110
Open Access Status DOI
Volume 8
Issue 110
Start page 1
End page 8
Total pages 8
Place of publication London
Publisher Biomed Central
Collection year 2008
Language eng
Subject C1
270899 Biotechnology not elsewhere classified
780105 Biological sciences
Abstract Background: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping. Results: Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively. Conclusion: Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches.
Keyword Biochemical Research Methods
Biotechnology & Applied Microbiology
Subcellular Location Patterns
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Mon, 18 Feb 2008, 16:47:38 EST