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BMCBI
2007

Fast automated cell phenotype image classification

11 years 5 months ago
Fast automated cell phenotype image classification
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 ...
Nicholas A. Hamilton, Radosav S. Pantelic, Kelly H
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Nicholas A. Hamilton, Radosav S. Pantelic, Kelly Hanson, Rohan D. Teasdale
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