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Hybrid segmentation method with confidence region detection for tumor identification

Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of inten...

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Main Authors:	Ejaz, Khurram, Mohd. Rahim, Mohd. Shafry, Bajwa, Usama Ijaz, Chaudhry, Huma, Rehman, Amjad, Ejaz, Farhan
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/90593/1/MohdShafryMohdRahim2020_HybridSegmentationMethodWithConfidenceRegion.pdf http://eprints.utm.my/id/eprint/90593/ http://dx.doi.org/10.1109/ACCESS.2020.3016627
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spelling	my.utm.905932021-04-30T14:48:11Z http://eprints.utm.my/id/eprint/90593/ Hybrid segmentation method with confidence region detection for tumor identification Ejaz, Khurram Mohd. Rahim, Mohd. Shafry Bajwa, Usama Ijaz Chaudhry, Huma Rehman, Amjad Ejaz, Farhan QA75 Electronic computers. Computer science Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art. Institute of Electrical and Electronics Engineers Inc. 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90593/1/MohdShafryMohdRahim2020_HybridSegmentationMethodWithConfidenceRegion.pdf Ejaz, Khurram and Mohd. Rahim, Mohd. Shafry and Bajwa, Usama Ijaz and Chaudhry, Huma and Rehman, Amjad and Ejaz, Farhan (2020) Hybrid segmentation method with confidence region detection for tumor identification. IEEE Access, 9 . pp. 35256-35278. ISSN 21693536 http://dx.doi.org/10.1109/ACCESS.2020.3016627
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	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Ejaz, Khurram Mohd. Rahim, Mohd. Shafry Bajwa, Usama Ijaz Chaudhry, Huma Rehman, Amjad Ejaz, Farhan Hybrid segmentation method with confidence region detection for tumor identification
description	Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.
format	Article
author	Ejaz, Khurram Mohd. Rahim, Mohd. Shafry Bajwa, Usama Ijaz Chaudhry, Huma Rehman, Amjad Ejaz, Farhan
author_facet	Ejaz, Khurram Mohd. Rahim, Mohd. Shafry Bajwa, Usama Ijaz Chaudhry, Huma Rehman, Amjad Ejaz, Farhan
author_sort	Ejaz, Khurram
title	Hybrid segmentation method with confidence region detection for tumor identification
title_short	Hybrid segmentation method with confidence region detection for tumor identification
title_full	Hybrid segmentation method with confidence region detection for tumor identification
title_fullStr	Hybrid segmentation method with confidence region detection for tumor identification
title_full_unstemmed	Hybrid segmentation method with confidence region detection for tumor identification
title_sort	hybrid segmentation method with confidence region detection for tumor identification
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2020
url	http://eprints.utm.my/id/eprint/90593/1/MohdShafryMohdRahim2020_HybridSegmentationMethodWithConfidenceRegion.pdf http://eprints.utm.my/id/eprint/90593/ http://dx.doi.org/10.1109/ACCESS.2020.3016627
_version_	1698696957965369344
score	13.211869

Hybrid segmentation method with confidence region detection for tumor identification

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