Enhancement of Multichannel Chromosome Classification Using a Region-Based Classifier and Vector Median Filtering
| dc.contributor.author | Karvelis, P. S. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Tsalikakis, D. G. | en |
| dc.contributor.author | Georgiou, I. A. | en |
| dc.date.accessioned | 2015-11-24T17:34:03Z | |
| dc.date.available | 2015-11-24T17:34:03Z | |
| dc.identifier.issn | 1089-7771 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13923 | |
| dc.rights | Default Licence | - |
| dc.subject | bayes rule | en |
| dc.subject | chromosome | en |
| dc.subject | vector median filter (vmf) | en |
| dc.subject | watershed transform (wt) | en |
| dc.subject | in-situ hybridization | en |
| dc.subject | color images | en |
| dc.subject | directional filters | en |
| dc.subject | m-fish | en |
| dc.subject | identification | en |
| dc.subject | segmentation | en |
| dc.subject | localization | en |
| dc.subject | algorithm | en |
| dc.subject | noise | en |
| dc.title | Enhancement of Multichannel Chromosome Classification Using a Region-Based Classifier and Vector Median Filtering | en |
| heal.abstract | Multichannel chromosome image acquisition is used for cancer diagnosis and research on genetic disorders. This type of imaging, apart from aiding the cytogeneticist in several ways, facilitates the visual detection of chromosome abnormalities. However, chromosome misclassification errors result from different factors, such as uneven hybridization, spectral overlap among fluors, and biochemical noise. In this paper, we enhance the chromosome classification accuracy by making use of a region Bayes classifier that increases the classification accuracy when compared to the already developed pixel-by-pixel classifier and by incorporating the vector median filtering approach for filtering of the image. The method is evaluated using a publicly available database that contains 183 six-channel chromosome sets of images. The overall improvement on the chromosome classification accuracy is 9.99%, compared to the pixel-by-pixel classifier without filtering. This improvement in the chromosome classification accuracy would allow subtle deoxyribonucleic acid abnormalities to be identified easily. The efficiency of the method might further improve by using features extracted from each region and a more sophisticated classifier. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | Doi 10.1109/Titb.2008.2008716 | - |
| heal.identifier.secondary | <Go to ISI>://000267835800019 | - |
| heal.journalName | Ieee Transactions on Information Technology in Biomedicine | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2009 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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