Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data

Luthfi Atikah; Novrindah Alvi Hasanah; Riyanarto Sarno; Aziz Fajar; Dewi Rahmawati

doi:10.1109/iSemantic50169.2020.9234303

Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data

Luthfi Atikah, Novrindah Alvi Hasanah, Riyanarto Sarno, Aziz Fajar, Dewi Rahmawati

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

Nowadays, many methods have been applied for brain segmentation on MRI data. This paper proposes a new method for brain segmentation using Adaptive Thresholding, K-Means Clustering, and Morphological Mathematics in MRI data. The adaptive threshold was chosen because the adaptive threshold method will vary across images to suit various lighting conditions and background changes. We segment the corpus callosum. This experiment shows that with the Adaptive Thresholding, K-Means Clustering, and Mathematical Morphology to segment the corpus callosum produces the highest Dice Similarity Coefficient (DSC) value of 0.757.

Original language	English
Title of host publication	Proceedings - 2020 International Seminar on Application for Technology of Information and Communication
Subtitle of host publication	IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	161-167
Number of pages	7
ISBN (Electronic)	9781728190686
DOIs	https://doi.org/10.1109/iSemantic50169.2020.9234303
Publication status	Published - 19 Sept 2020
Event	2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020 - Semarang, Indonesia Duration: 19 Sept 2020 → 20 Sept 2020

Publication series

Name	Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020

Conference

Conference	2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020
Country/Territory	Indonesia
City	Semarang
Period	19/09/20 → 20/09/20

Keywords

Adaptive Thresholding
Brain Segmentation
Corpus Callosum
K-Means Clustering
MRI data
Mathematical Morphology

Access to Document

10.1109/iSemantic50169.2020.9234303

Cite this

Atikah, L., Hasanah, N. A., Sarno, R., Fajar, A., & Rahmawati, D. (2020). Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data. In Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020 (pp. 161-167). Article 9234303 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iSemantic50169.2020.9234303

Atikah, Luthfi ; Hasanah, Novrindah Alvi ; Sarno, Riyanarto et al. / Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data. Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 161-167 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020).

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abstract = "Nowadays, many methods have been applied for brain segmentation on MRI data. This paper proposes a new method for brain segmentation using Adaptive Thresholding, K-Means Clustering, and Morphological Mathematics in MRI data. The adaptive threshold was chosen because the adaptive threshold method will vary across images to suit various lighting conditions and background changes. We segment the corpus callosum. This experiment shows that with the Adaptive Thresholding, K-Means Clustering, and Mathematical Morphology to segment the corpus callosum produces the highest Dice Similarity Coefficient (DSC) value of 0.757.",

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author = "Luthfi Atikah and Hasanah, {Novrindah Alvi} and Riyanarto Sarno and Aziz Fajar and Dewi Rahmawati",

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Atikah, L, Hasanah, NA, Sarno, R, Fajar, A & Rahmawati, D 2020, Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data. in Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020., 9234303, Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020, Institute of Electrical and Electronics Engineers Inc., pp. 161-167, 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020, Semarang, Indonesia, 19/09/20. https://doi.org/10.1109/iSemantic50169.2020.9234303

Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data. / Atikah, Luthfi; Hasanah, Novrindah Alvi; Sarno, Riyanarto et al.
Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 161-167 9234303 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hasanah, Novrindah Alvi

AU - Sarno, Riyanarto

AU - Fajar, Aziz

AU - Rahmawati, Dewi

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AB - Nowadays, many methods have been applied for brain segmentation on MRI data. This paper proposes a new method for brain segmentation using Adaptive Thresholding, K-Means Clustering, and Morphological Mathematics in MRI data. The adaptive threshold was chosen because the adaptive threshold method will vary across images to suit various lighting conditions and background changes. We segment the corpus callosum. This experiment shows that with the Adaptive Thresholding, K-Means Clustering, and Mathematical Morphology to segment the corpus callosum produces the highest Dice Similarity Coefficient (DSC) value of 0.757.

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KW - K-Means Clustering

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Atikah L, Hasanah NA, Sarno R, Fajar A, Rahmawati D. Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data. In Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 161-167. 9234303. (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020). doi: 10.1109/iSemantic50169.2020.9234303

Brain segmentation using adaptive thresholding, k-means clustering and mathematical morphology in MRI Data

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