𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Robust adaptive spot segmentation of DNA microarray images

✍ Scribed by Alan Wee-Chung Liew; Hong Yan; Mengsu Yang


Book ID
104161650
Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
604 KB
Volume
36
Category
Article
ISSN
0031-3203

No coin nor oath required. For personal study only.

✦ Synopsis


The rapid advancement of DNA chip (microarray) technology has revolutionalized genetic research in bioscience. However, the enormous amount of data produced from a microarray image makes automatic computer analysis indispensable. An important ΓΏrst step in analyzing microarray image is the accurate determination of the DNA spots in the image. We report here a novel spot segmentation method for DNA microarray images. The algorithm makes use of adaptive thresholding and statistical intensity modeling to: (i) generate the grid structure automatically, where each subregion in the grid contains only one spot, and (ii) to segment the spot, if any, within each subregion. The algorithm is fully automatic, robust, and can aid in the high throughput computer analysis of microarray data.


πŸ“œ SIMILAR VOLUMES


Robustness of noisy and blurry images se
✍ I. V. Gribkov; P. P. Koltsov; N. V. Kotovich; A. A. Kravchenko; A. S. Kutsaev; A πŸ“‚ Article πŸ“… 2009 πŸ› SP MAIK Nauka/Interperiodica 🌐 English βš– 383 KB
Multi-level adaptive segmentation of mul
✍ A. Zavaljevski; A.P. Dhawan; M. Gaskil; W. Ball; J.D. Johnson πŸ“‚ Article πŸ“… 2000 πŸ› Elsevier Science 🌐 English βš– 709 KB

MR brain image segmentation into several tissue classes is of significant interest to visualize and quantify individual anatomical structures. Traditionally, the segmentation is performed manually in a clinical environment that is operator dependent and may be difficult to reproduce. Though several

Intensity-adaptive segmentation of singl
✍ Reza Momenan; Daniel Hommer; Robert Rawlings; Urs Ruttimann; Michael Kerich; Dan πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 315 KB

A procedure for segmentation of intracranial tissues, including cerebrospinal fluid surrounding the brain, cortical and subcortical gray matter, and white matter, in a T 1 -weighted magnetic resonance image of the brain, has been developed. The proposed method utilizes information from the histogram