Further information Microarray Image Analysis Project

Ad-hoc Segmentation Piprline for Microarray Image Analysis, to appear in IS&T-SPIE, Electronic Imaging 2006, San Jose - California USA (pdf)
Statistical Algorithms For Microarray Image Analysis , internal IPLAB Report (pdf)

Ad-Hoc Segmentation Pipeline for Microarray Image Analysis
to appear in IS&T-SPIE, Electronic Imaging 2006, San Jose - California USA

Sebastiano Battiato, Gianpiero Di Blasi, Giovanni Maria Farinella, Giovanni Gallo, Giuseppe Claudio Guarnera
{battiato, gdiblasi, gfarinella, gallo}@dmi.unict.it
g.guarnera@studenti.unict.it

IPLab – Image Processing Laboratory
http://www.dmi.unict.it/~iplab
Dipartimento di Matematica e Informatica
University of Catania, Via Andrea Doria 6 – 95125, Catania (Italy)

FULL PAPER

Figure 1. Tipical Microarray Technology phases

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. The Microarray Technology consists of different sequential phases that are shown in Figure 1. We propose a new advanced segmentation pipeline called MISP (Microarray Image Segmentation Pipeline). MISP steps and semantic segmentation regions discovered using the pipeline are showed in the figures below. The MISP Software Architecture (Figure 6) include Visualization, Segmentation, information and quality measure extraction. Preliminary results showed how the proposed pipeline is able to capture in a more reliable way the underlying signal distribution of input data.

Figure 2. MISP: Microarray Image Segmentation Pipeline
Cyan is dashed line refers to Spot-Background Separation block, while Green is refers to Foreground and Local Background identification.

Figure 3. Red Channel Spot – Background separation

Figure 4. Channel Foreground and local background identification

Figure 5. Microarray Image Semantic Color Region.
Background (black), Local Background (blue), Red Channel Foreground (red),
Green Channel Foreground (green), Red Channel and Green Channel Foreground (yellow).

Figure 6. MISP: software prototype architecture

Figure 7. MISP vs. Scanalyze visual Comparison

Figure 8. Misp vs. Scanalyze Analytical Comparision

STATISTICAL ALGORITHMS FOR MICROARRAY IMAGE ANALYSIS
internal IPLAB Report

Sebastiano Battiato, Gianpiero Di Blasi, Giovanni Maria Farinella, Giovanni Gallo, Giuseppe Claudio Guarnera
{battiato, gdiblasi, gfarinella, gallo}@dmi.unict.it
g.guarnera@studenti.unict.it

IPLab – Image Processing Laboratory
http://www.dmi.unict.it/~iplab
Dipartimento di Matematica e Informatica
University of Catania, Via Andrea Doria 6 – 95125, Catania (Italy)

REPORT

In this paper novel techniques for Microarray Image Analysis are proposed. In particular, we describe MIRA (Microarray Image Rotation Algorithm) and SGRIP (Statistical GRIdding Pipeline) statistical algorithms respectively devoted to restore the original microarray orientation and detect the correct geometrical information about each spot of input Microarray. Both solutions significantly improve the performances of the segmentation pipeline MISP (Microarray Image Segmentation Pipeline). MIRA, SGRIP and MISP modules have been developed as plug-ins for an advanced on-going framework for Microarray Image Analysis. Experiments confirm the effectiveness of the proposed techniques, in terms of visual and numerical data.

SGRIP Experiment Example 1