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Hybrid GrabCut Hidden Markov Model for segmentation

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associ...

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Main Authors: Saeed, Soobia, Abdullah, Afnizanfaizal, Jhanjhi, N. Z., Naqvi, Mehmood, Masud, Mehedi, AlZain, Mohammed A.
Format: Article
Language:English
Published: Tech Science Press 2022
Subjects:
QA76 Computer software
Online Access:http://eprints.utm.my/103259/1/AfnizanfaizalAbdullah2022_HybridGrabCutHiddenMarkovModel.pdf
http://eprints.utm.my/103259/
http://dx.doi.org/10.32604/cmc.2022.024085
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spelling my.utm.1032592023-10-24T10:05:22Z http://eprints.utm.my/103259/ Hybrid GrabCut Hidden Markov Model for segmentation Saeed, Soobia Abdullah, Afnizanfaizal Jhanjhi, N. Z. Naqvi, Mehmood Masud, Mehedi AlZain, Mohammed A. QA76 Computer software Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images. This research proposed the new framework of the hybrid k-Nearest Neighbors (k-NN) model that is a combination of hybridization of Graph Cut and Support Vector Machine (GCSVM) and Hidden Markov Model of k-Mean Clustering Algorithm (HMMkC). There are four different methods are used in this research namely (1) SVM, (2) GrabCut segmentation, (3) HMM, and (4) k-mean clustering algorithm. In this framework, on the one hand, phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance. This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform. On the other hand, in phase two, segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode (ICMM) with identifying the range of distant. Comparative evaluation is also performing by the comparison of existing techniques in this research. In conclusion, our proposed model gives better results than existing. This proposed model helps to common man and doctor that can identify their condition of brain easily. In future, this will model will use for other brain related diseases. Tech Science Press 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/103259/1/AfnizanfaizalAbdullah2022_HybridGrabCutHiddenMarkovModel.pdf Saeed, Soobia and Abdullah, Afnizanfaizal and Jhanjhi, N. Z. and Naqvi, Mehmood and Masud, Mehedi and AlZain, Mohammed A. (2022) Hybrid GrabCut Hidden Markov Model for segmentation. Computers, Materials and Continua, 72 (1). pp. 851-869. ISSN 1546-2218 http://dx.doi.org/10.32604/cmc.2022.024085 DOI : 10.32604/cmc.2022.024085
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Saeed, Soobia
Abdullah, Afnizanfaizal
Jhanjhi, N. Z.
Naqvi, Mehmood
Masud, Mehedi
AlZain, Mohammed A.
Hybrid GrabCut Hidden Markov Model for segmentation
description Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images. This research proposed the new framework of the hybrid k-Nearest Neighbors (k-NN) model that is a combination of hybridization of Graph Cut and Support Vector Machine (GCSVM) and Hidden Markov Model of k-Mean Clustering Algorithm (HMMkC). There are four different methods are used in this research namely (1) SVM, (2) GrabCut segmentation, (3) HMM, and (4) k-mean clustering algorithm. In this framework, on the one hand, phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance. This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform. On the other hand, in phase two, segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode (ICMM) with identifying the range of distant. Comparative evaluation is also performing by the comparison of existing techniques in this research. In conclusion, our proposed model gives better results than existing. This proposed model helps to common man and doctor that can identify their condition of brain easily. In future, this will model will use for other brain related diseases.
format Article
author Saeed, Soobia
Abdullah, Afnizanfaizal
Jhanjhi, N. Z.
Naqvi, Mehmood
Masud, Mehedi
AlZain, Mohammed A.
author_facet Saeed, Soobia
Abdullah, Afnizanfaizal
Jhanjhi, N. Z.
Naqvi, Mehmood
Masud, Mehedi
AlZain, Mohammed A.
author_sort Saeed, Soobia
title Hybrid GrabCut Hidden Markov Model for segmentation
title_short Hybrid GrabCut Hidden Markov Model for segmentation
title_full Hybrid GrabCut Hidden Markov Model for segmentation
title_fullStr Hybrid GrabCut Hidden Markov Model for segmentation
title_full_unstemmed Hybrid GrabCut Hidden Markov Model for segmentation
title_sort hybrid grabcut hidden markov model for segmentation
publisher Tech Science Press
publishDate 2022
url http://eprints.utm.my/103259/1/AfnizanfaizalAbdullah2022_HybridGrabCutHiddenMarkovModel.pdf
http://eprints.utm.my/103259/
http://dx.doi.org/10.32604/cmc.2022.024085
_version_ 1781777669801639936
score 13.211869

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