NDLI: Intensity-based segmentation of microarray images

Content Provider	IEEE Xplore Digital Library
Author	Nagarajan, R.
Copyright Year	1982
Abstract	The underlying principle in microarray image analysis is that the spot intensity is a measure of the gene expression. This implicitly assumes the gene expression of a spot to be governed entirely by the distribution of the pixel intensities. Thus, a segmentation technique based on the distribution of the pixel intensities is appropriate for the current problem. In this paper, clustering-based segmentation is described to extract the target intensity of the spots. The approximate boundaries of the spots in the microarray are determined by manual adjustment of rectilinear grids. The distribution of the pixel intensity in a grid containing a spot is assumed to be the superposition of the foreground and the local background. The k-means clustering technique and the partitioning around medoids (PAM) were used to generate a binary partition of the pixel intensity distribution. The median (k-means) and the medoid (PAM) of the cluster members are chosen as the cluster representatives. The effectiveness of the clustering-based segmentation techniques was tested on publicly available arrays generated in a lipid metabolism experiment (Callow et al., 2000). The results are compared against those obtained using the region-growing approach (SPOT) (Yang et al., 2001). The effect of additive white Gaussian noise is also investigated.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Nuclear and Plasma Sciences Society IEEE Signal Processing Society IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
Page Count	8
File Size	484844
Starting Page	882
Ending Page	889
File Format	PDF
ISSN	02780062
Volume Number	22
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Probes DNA Gene expression Fluorescence Switches Image color analysis Testing Lipidomics Biochemistry
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Radiological and Ultrasound Technology Software

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