Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/13584Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Giannakeas, N. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:31:26Z | - |
| dc.date.available | 2015-11-24T17:31:26Z | - |
| dc.identifier.issn | 0895-6111 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13584 | - |
| dc.rights | Default Licence | - |
| dc.subject | microarray image processing | en |
| dc.subject | gridding | en |
| dc.subject | segmentation | en |
| dc.subject | k-means | en |
| dc.subject | fuzzy c means | en |
| dc.subject | DNA microarray | en |
| dc.subject | gene-expression | en |
| dc.title | An automated method for gridding and clustering-based segmentation of cDNA microarray images | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.compmedimag.2008.10.003 | - |
| heal.identifier.secondary | <Go to ISI>://000262692200006 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0895611108001018/1-s2.0-S0895611108001018-main.pdf?_tid=e492477cb5a849ab7666b7644698c4ec&acdnat=1339757179_18fa408075624e9a45cd06f474c31b88 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2009 | - |
| heal.abstract | Microarrays are widely used to quantify gene expression levels. Microarray image analysis is one of the tools, which are necessary when dealing with vast amounts of biological data. In this work we propose a new method for the automated analysis of microarray images. The proposed method consists of two stages: gridding and segmentation. Initially, the microarray images are preprocessed using template matching, and block and spot finding takes place. Then, the non-expressed spots are detected and a grid is fit on the image using a Voronoi diagram. In the segmentation stage, K-means and Fuzzy C means (FCM) Clustering are employed. The proposed method was evaluated using images from the Stanford Microarray Database (SMD). The results that are presented in the segmentation stage show the efficiency of our Fuzzy C means-based work compared to the two already developed K-means-based methods. The proposed method can handle images with artefacts and it is fully automated. (C) 2008 Elsevier Ltd. All rights reserved. | en |
| heal.publisher | Elsevier | en |
| heal.journalName | Computerized Medical Imaging and Graphics | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Giannakeas-2009-An automated method.pdf | 1.53 MB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
