Texture Analysis Using 3D Gabor Features and 3D MPEG-7 Edge Histogram Descriptor in Fluorescence Microscopy

MAJTNER, Tomáš and David SVOBODA. Texture Analysis Using 3D Gabor Features and 3D MPEG-7 Edge Histogram Descriptor in Fluorescence Microscopy. Online. In 4th International Conference on 3D Imaging (IC3D). Los Alamitos, California: IEEE Computer Society, 2014, p. 1-7. ISBN 978-1-4799-8023-9. Available from: https://dx.doi.org/10.1109/IC3D.2014.7032576.

Basic information

• Original name: Texture Analysis Using 3D Gabor Features and 3D MPEG-7 Edge Histogram Descriptor in Fluorescence Microscopy
• Authors: MAJTNER, Tomáš (703 Slovakia, belonging to the institution) and David SVOBODA (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Los Alamitos, California, 4th International Conference on 3D Imaging (IC3D), p. 1-7, 7 pp. 2014.
• Publisher: IEEE Computer Society

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 20200 2.2 Electrical engineering, Electronic engineering, Information engineering
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• RIV identification code: RIV/00216224:14330/14:00074031
• Organization unit: Faculty of Informatics
• ISBN: 978-1-4799-8023-9
• Doi: http://dx.doi.org/10.1109/IC3D.2014.7032576
• UT WoS: 000380557000005
• Keywords in English: 3D images; Edge Histogram Descriptor; Gabor filters; fluorescence microscopy
• Tags: cbia-web
• Tags: International impact, Reviewed

Abstract

The pattern recognition with focus on texture and shape analysis is still very hot topic, especially in biomedical image processing. In this article, we introduce 3D extension of well-known approaches for this particular area. We focus on the collection of MPEG-7 image descriptors, specifically on Edge Histogram Descriptor (EHD) and Gabor features, which are core of Homogeneous Texture Descriptor (HTD). The proposed extensions are evaluated on the dataset consisting of three classes of 3D volumetric biomedical images. Two different classifiers, namely $k$-NN and Multi-Class SVM, are used to evaluate the proposed algorithms. According to the presented tests, proposed 3D extensions clearly outperforms their 2D equivalents in the classification tasks.

Links

• GA14-22461S, research and development project: Name: Vývoj a studium metod pro kvantifikaci živých buněk (Acronym: Live Cell Quantification)

Investor: Czech Science Foundation

• MUNI/A/0855/2013, interní kód MU: Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace III. (Acronym: FI MAV III.)

Investor: Masaryk University, Category A