Microarray is a new class of biotechnologies able to help biologist researches to extrapolate new knowledge from biological experiments. Image Analysis is devoted to extrapolate, process and visualize image information. For this reason it has found application also in Microarray, where it is a crucial step of this technology (e.g. segmentation). In this paper we describe MISP (Microarray Image Segmentation Pipeline), a new segmentation pipeline for Microarray Image Analysis. The pipeline uses a recent segmentation algorithm based on statistical analysis coupled with K-Means algorithm. The Spot masks produced by MISP are used to determinate spots information and quality measures. A software prototype system has been developed; it includes visualization, segmentation, information and quality measure extraction. Experiments show the effectiveness of the proposed pipeline both in terms of visual accuracy and measured quality values. Comparisons with existing solutions (e.g. Scanalyze [1]) confirm the improvement with respect to previously published works.

Ad-Hoc Segmentation Pipeline for Microarray Image Analysis

BATTIATO, SEBASTIANO;FARINELLA, GIOVANNI MARIA;GALLO, Giovanni;
2006-01-01

Abstract

Microarray is a new class of biotechnologies able to help biologist researches to extrapolate new knowledge from biological experiments. Image Analysis is devoted to extrapolate, process and visualize image information. For this reason it has found application also in Microarray, where it is a crucial step of this technology (e.g. segmentation). In this paper we describe MISP (Microarray Image Segmentation Pipeline), a new segmentation pipeline for Microarray Image Analysis. The pipeline uses a recent segmentation algorithm based on statistical analysis coupled with K-Means algorithm. The Spot masks produced by MISP are used to determinate spots information and quality measures. A software prototype system has been developed; it includes visualization, segmentation, information and quality measure extraction. Experiments show the effectiveness of the proposed pipeline both in terms of visual accuracy and measured quality values. Comparisons with existing solutions (e.g. Scanalyze [1]) confirm the improvement with respect to previously published works.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/99624
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