A modified fast local region based method for image segmentation

Among many different methods of image segmentation, local region based algorithms received noticeable attention. In particular, these algorithms can be practically useful for images with intensity inhomogeneity like medical ultrasound images and tumor images. Although local based algorithms, unlike...

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Main Authors: Astaraki, M., Aslian, H., Hamedi, M.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Subjects:
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author Astaraki, M.
Aslian, H.
Hamedi, M.
author_facet Astaraki, M.
Aslian, H.
Hamedi, M.
author_sort Astaraki, M.
collection ePrints
description Among many different methods of image segmentation, local region based algorithms received noticeable attention. In particular, these algorithms can be practically useful for images with intensity inhomogeneity like medical ultrasound images and tumor images. Although local based algorithms, unlike edge based methods, are insensitive to noise and weak edges, presence of severe noise in the images is still a problem. In this study, a modified local region based method is proposed for solving this problem, additionally the time consuming re-initialization process is removed by using the robust and fast Reaction Diffusion method. Conclusively, the final results reveal that the proposed modified algorithm can achieve more appropriate results in terms of accuracy and speed than five well-known local based methods.
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institution Universiti Teknologi Malaysia - ePrints
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spelling utm.eprints-733922017-11-21T08:17:09Z http://eprints.utm.my/73392/ A modified fast local region based method for image segmentation Astaraki, M. Aslian, H. Hamedi, M. QH Natural history Among many different methods of image segmentation, local region based algorithms received noticeable attention. In particular, these algorithms can be practically useful for images with intensity inhomogeneity like medical ultrasound images and tumor images. Although local based algorithms, unlike edge based methods, are insensitive to noise and weak edges, presence of severe noise in the images is still a problem. In this study, a modified local region based method is proposed for solving this problem, additionally the time consuming re-initialization process is removed by using the robust and fast Reaction Diffusion method. Conclusively, the final results reveal that the proposed modified algorithm can achieve more appropriate results in terms of accuracy and speed than five well-known local based methods. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item PeerReviewed Astaraki, M. and Aslian, H. and Hamedi, M. (2016) A modified fast local region based method for image segmentation. In: 4th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2015, 19-21 Oct 2015, Kuala Lumpur, Malaysia. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971590277&doi=10.1109%2fICSIPA.2015.7412220&partnerID=40&md5=14020bcc662fcec304bbfb1249b82663
spellingShingle QH Natural history
Astaraki, M.
Aslian, H.
Hamedi, M.
A modified fast local region based method for image segmentation
title A modified fast local region based method for image segmentation
title_full A modified fast local region based method for image segmentation
title_fullStr A modified fast local region based method for image segmentation
title_full_unstemmed A modified fast local region based method for image segmentation
title_short A modified fast local region based method for image segmentation
title_sort modified fast local region based method for image segmentation
topic QH Natural history
work_keys_str_mv AT astarakim amodifiedfastlocalregionbasedmethodforimagesegmentation
AT aslianh amodifiedfastlocalregionbasedmethodforimagesegmentation
AT hamedim amodifiedfastlocalregionbasedmethodforimagesegmentation
AT astarakim modifiedfastlocalregionbasedmethodforimagesegmentation
AT aslianh modifiedfastlocalregionbasedmethodforimagesegmentation
AT hamedim modifiedfastlocalregionbasedmethodforimagesegmentation