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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2018 - 14

A Novel Approach for Bi-Level Segmentation of Tuberculosis Bacilli Based on Meta-Heuristic Algorithms

AYAS, S. See more information about AYAS, S. on SCOPUS See more information about AYAS, S. on IEEExplore See more information about AYAS, S. on Web of Science, DOGAN, H. See more information about  DOGAN, H. on SCOPUS See more information about  DOGAN, H. on SCOPUS See more information about DOGAN, H. on Web of Science, GEDIKLI, E. See more information about  GEDIKLI, E. on SCOPUS See more information about  GEDIKLI, E. on SCOPUS See more information about GEDIKLI, E. on Web of Science, EKINCI, M. See more information about EKINCI, M. on SCOPUS See more information about EKINCI, M. on SCOPUS See more information about EKINCI, M. on Web of Science
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Download PDF pdficon (1,407 KB) | Citation | Downloads: 523 | Views: 3,450

Author keywords
computer aided analysis, heuristic algorithms, image segmentation, information entropy, particle swarm optimization

References keywords
tuberculosis(12), thresholding(10), segmentation(10), image(10), images(8), algorithm(7), algorithms(6), yang(5), sputum(5), method(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 113 - 120
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01014
Web of Science Accession Number: 000426449500014
SCOPUS ID: 85043229405

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Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 8,417 TCR
SCOPUS® Citations for all references: 21,782 TCR

Web of Science® Average Citations per reference: 222 ACR
SCOPUS® Average Citations per reference: 573 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-01-24 06:01 in 201 seconds.

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