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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tech Science Press
2022
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| Online Access: | http://eprints.utm.my/103259/1/AfnizanfaizalAbdullah2022_HybridGrabCutHiddenMarkovModel.pdf |
| _version_ | 1796867425913798656 |
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| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T21:27:04Z |
| format | Article |
| id | utm.eprints-103259 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:27:04Z |
| publishDate | 2022 |
| publisher | Tech Science Press |
| record_format | dspace |
| spelling | utm.eprints-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 |
| 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 |
| title | 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_short | Hybrid GrabCut Hidden Markov Model for segmentation |
| title_sort | hybrid grabcut hidden markov model for segmentation |
| topic | QA76 Computer software |
| url | http://eprints.utm.my/103259/1/AfnizanfaizalAbdullah2022_HybridGrabCutHiddenMarkovModel.pdf |
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