Distinguishing of different tissue types using K-Means clustering of color segmentation

Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zinah R. Hussein, Ans Ibrahim Mahameed, Jawaher Abdulwahab Fadhil
Formato:	article
Lenguaje:	EN RU UK
Publicado:	PC Technology Center 2021
Materias:	image analysis tissue image segmentation k-means clustering color-based segmentation Technology (General) T1-995 Industry HD2321-4730.9
Acceso en línea:	https://doaj.org/article/32fecde6d2304fd2a4b6cceb6d786ca0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:32fecde6d2304fd2a4b6cceb6d786ca0
record_format	dspace
spelling	oai:doaj.org-article:32fecde6d2304fd2a4b6cceb6d786ca02021-11-04T14:06:13ZDistinguishing of different tissue types using K-Means clustering of color segmentation1729-37741729-406110.15587/1729-4061.2021.242491https://doaj.org/article/32fecde6d2304fd2a4b6cceb6d786ca02021-10-01T00:00:00Zhttp://journals.uran.ua/eejet/article/view/242491https://doaj.org/toc/1729-3774https://doaj.org/toc/1729-4061Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in distinguishing between different tissue types. This work offers a clustering-based color segmentation approach for medical images that can successfully find the core points of clusters through penetrating the red-green-blue (RGB) pairings without previous information. Here, the number of RGB pairs functions as a clusters’ number to increase the accuracy of current algorithms by establishing the automated initialization settings for conventional K-Means clustering algorithms. On a picture of tissue stained with eosin and hematoxylin, the developed K-Means clustering technique is used in this study (H&E). The blue items are found in Cluster 3. There are things in both light and dark blue. The results showed that the proposed technique can differentiate light blue from dark blue employing the 'L' layer in Lab Color Space (Lab* CS). The work recognized the cells' nuclei with a dark blue color successfully. As a result, this approach may aid in precisely diagnosing the stage of tumor invasion and guiding clinical therapiesZinah R. HusseinAns Ibrahim MahameedJawaher Abdulwahab FadhilPC Technology Centerarticleimage analysistissue image segmentationk-means clusteringcolor-based segmentationTechnology (General)T1-995IndustryHD2321-4730.9ENRUUKEastern-European Journal of Enterprise Technologies, Vol 5, Iss 2 (113), Pp 22-28 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	image analysis tissue image segmentation k-means clustering color-based segmentation Technology (General) T1-995 Industry HD2321-4730.9
spellingShingle	image analysis tissue image segmentation k-means clustering color-based segmentation Technology (General) T1-995 Industry HD2321-4730.9 Zinah R. Hussein Ans Ibrahim Mahameed Jawaher Abdulwahab Fadhil Distinguishing of different tissue types using K-Means clustering of color segmentation
description	Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in distinguishing between different tissue types. This work offers a clustering-based color segmentation approach for medical images that can successfully find the core points of clusters through penetrating the red-green-blue (RGB) pairings without previous information. Here, the number of RGB pairs functions as a clusters’ number to increase the accuracy of current algorithms by establishing the automated initialization settings for conventional K-Means clustering algorithms. On a picture of tissue stained with eosin and hematoxylin, the developed K-Means clustering technique is used in this study (H&E). The blue items are found in Cluster 3. There are things in both light and dark blue. The results showed that the proposed technique can differentiate light blue from dark blue employing the 'L' layer in Lab Color Space (Lab* CS). The work recognized the cells' nuclei with a dark blue color successfully. As a result, this approach may aid in precisely diagnosing the stage of tumor invasion and guiding clinical therapies
format	article
author	Zinah R. Hussein Ans Ibrahim Mahameed Jawaher Abdulwahab Fadhil
author_facet	Zinah R. Hussein Ans Ibrahim Mahameed Jawaher Abdulwahab Fadhil
author_sort	Zinah R. Hussein
title	Distinguishing of different tissue types using K-Means clustering of color segmentation
title_short	Distinguishing of different tissue types using K-Means clustering of color segmentation
title_full	Distinguishing of different tissue types using K-Means clustering of color segmentation
title_fullStr	Distinguishing of different tissue types using K-Means clustering of color segmentation
title_full_unstemmed	Distinguishing of different tissue types using K-Means clustering of color segmentation
title_sort	distinguishing of different tissue types using k-means clustering of color segmentation
publisher	PC Technology Center
publishDate	2021
url	https://doaj.org/article/32fecde6d2304fd2a4b6cceb6d786ca0
work_keys_str_mv	AT zinahrhussein distinguishingofdifferenttissuetypesusingkmeansclusteringofcolorsegmentation AT ansibrahimmahameed distinguishingofdifferenttissuetypesusingkmeansclusteringofcolorsegmentation AT jawaherabdulwahabfadhil distinguishingofdifferenttissuetypesusingkmeansclusteringofcolorsegmentation
_version_	1718444829869867008

Distinguishing of different tissue types using K-Means clustering of color segmentation

Ejemplares similares