Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

Deshpande, NM; Gite, S; Pradhan, B; Kotecha, K; Alamri, A

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

Deshpande, NM Gite, S Pradhan, B

Kotecha, K Alamri, A

Permalink

Publisher:: American Institute of Mathematical Sciences (AIMS)
Publication Type:: Journal Article
Citation:: Math Biosci Eng, 2022, 19, (2), pp. 1970-2001
Issue Date:: 2022-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.69 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Deshpande, NM
dc.contributor.author	Gite, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Kotecha, K
dc.contributor.author	Alamri, A
dc.date.accessioned	2022-03-16T21:58:24Z
dc.date.available	2022-03-16T21:58:24Z
dc.date.issued	2022-01
dc.identifier.citation	Math Biosci Eng, 2022, 19, (2), pp. 1970-2001
dc.identifier.issn	1547-1063
dc.identifier.issn	1551-0018
dc.identifier.uri	http://hdl.handle.net/10453/155279
dc.description.abstract	The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	American Institute of Mathematical Sciences (AIMS)
dc.relation.ispartof	Math Biosci Eng
dc.relation.isbasedon	10.3934/mbe.2022093
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0903 Biomedical Engineering, 0904 Chemical Engineering
dc.subject.classification	Bioinformatics
dc.title	Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0903 Biomedical Engineering
utslib.for	0904 Chemical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-16T21:58:22Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	2

Abstract:

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155279