Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/10353
Title: A wavelet - based markov random field segmentation model in segmenting microarray experiments
Authors: Αθανασιάδης, Εμμανουήλ Ι.
Κάβουρας, Διονύσης Α.
Κωστόπουλος, Σπυρίδων
Γκλώτσος, Δημήτριος
Καλατζής, Ιωάννης
Contributors: Νικηφορίδης, Γεώργιος Σ.
Item type: Journal article
Keywords: Image segmentation;Wavelets (Mathematics);Κατάτμηση εικόνας;Κυμάτιο
Subjects: Technology
Biomedical engineering
Τεχνολογία
Βιοϊατρική τεχνολογία
Issue Date: 14-May-2015
2011
Publisher: Elsevier Ireland Ltd
Abstract: In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r2), and the concordance correlation (pc) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r2, and pc (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.
Language: English
Citation: Athanasiadis, E., Cavouras, D., Kostopoulos, S., Glotsos, D., Kalatzis, I., et al. (December 2011). A wavelet - based markov random field segmentation model in segmenting microarray experiments. Computer Methods and Programs in Biomedicine. 104(3). pp. 307-315. Elsevier Ireland Ltd: 2011.
Journal: Computer Methods and Programs in Biomedicine
Type of Journal: With a review process (peer review)
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/10353
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