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An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique

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Main Authors: Fahmi Akmal, Dzulkifli, Mohd Yusoff, Mashor, Hasnan, Jaafar
Other Authors: fahmiakmaldzulkifli@gmail.com
Format: Article
Language:English
Published: IOP Publishing 2020
Subjects:
Meningioma
Brain tumours
Ki67 cells
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69034
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spelling my.unimap-690342020-12-16T08:35:31Z An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique Fahmi Akmal, Dzulkifli Mohd Yusoff, Mashor Hasnan, Jaafar fahmiakmaldzulkifli@gmail.com Meningioma Brain tumours Ki67 cells Link to publisher's homepage at https://iopscience.iop.org/ Meningioma is a type of primary brain tumours. The meningiomas account for about one-third of all primary brain tumours. Image segmentation plays an important role in image analysis, especially detecting the tumours or cancerous areas in medical images. The output images from the segmentation prominently affect the system in detecting the tumour cells. Currently, the pathologists use the ‘eye-balling’ estimation technique to count the Ki67 cells. This technique was known as a time-saving measure. However, it has poor reliability and accuracy in counting the Ki67 cells. This paper proposed an automatic Ki67 cell counting in meningioma by using k-means clustering approach. The k-means clustering was used to segment the Ki67 cells and then the cells were classified into positive and negative Ki67 cells. The proposed system has been tested on 12 histopathological meningioma images. The proposed system is compared to the manually segmented images that have been validated in prior by the pathologists. The results show that the proposed system was able to segment the Ki67 cells with an average accuracy of 95.29%. The sensitivity and specificity of the proposed system were also high with an average of 93.56% and 97.39%, respectively 2020-12-16T08:35:31Z 2020-12-16T08:35:31Z 2019 Article Journal of Physics: Conference Series, vol.1372, 2019, 7 pages 1742-6588 (print) 1742-6596 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69034 https://iopscience.iop.org/issue/1742-6596/1372/1 en International Conference on Biomedical Engineering (ICoBE); IOP Publishing
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Meningioma
Brain tumours
Ki67 cells
spellingShingle Meningioma
Brain tumours
Ki67 cells
Fahmi Akmal, Dzulkifli
Mohd Yusoff, Mashor
Hasnan, Jaafar
An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
description Link to publisher's homepage at https://iopscience.iop.org/
author2 fahmiakmaldzulkifli@gmail.com
author_facet fahmiakmaldzulkifli@gmail.com
Fahmi Akmal, Dzulkifli
Mohd Yusoff, Mashor
Hasnan, Jaafar
format Article
author Fahmi Akmal, Dzulkifli
Mohd Yusoff, Mashor
Hasnan, Jaafar
author_sort Fahmi Akmal, Dzulkifli
title An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
title_short An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
title_full An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
title_fullStr An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
title_full_unstemmed An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
title_sort automated segmentation and counting of ki67 cells in meningioma using k-means clustering technique
publisher IOP Publishing
publishDate 2020
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69034
_version_ 1698698546697469952
score 13.214268

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