Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators
Image segmentation refers to the process of partitioning a digital image into multiple sets of pixels are known as segments. The main goal of image segmentation is to change and simplify the representation of an image into something that is more meaningful and easier to analyze. The manual transacti...
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my.unimas.ir.93152023-05-16T03:12:08Z http://ir.unimas.my/id/eprint/9315/ Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators Arshad, Javed Wang, Yin Chai Abdulhameed, Rakan Alenezi Narayanan, Kulathu Ramaiyer TR Photography Image segmentation refers to the process of partitioning a digital image into multiple sets of pixels are known as segments. The main goal of image segmentation is to change and simplify the representation of an image into something that is more meaningful and easier to analyze. The manual transactions for segmentation by experts is a difficult phenomena and time consuming process as well as. Most of the images in the process received are lacking of good quality. The main objective of this study is to develop a reliable mechanism to enhance the image quality and extract the abnormal portion through brain MR image accurately. A spatial filter is designed by utilizing the spatial information of the image and further to use collective information to enhance the poor quality of image(s), whereas, k-means clustering and mathematical morphological operations which extract the tumor segment from images. The proposed method is applied on different types of brain MR images for both visual and quantitative evaluations. Experimental results concluded during the practicum showed promising and reliable accuracy to open a thorough research for better future perspective of the technique developed in the article. Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators. Available from: http://www.researchgate.net/publication/265294217_Fully_Automatic_Detections_of_Abnormalities_of_Brain_MR_Images_by_utilizing_Spatial_Information_and_Mathematical_Morphological_Operators [accessed Nov 3, 2015]. Natural Sciences Publishing Cor. 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/9315/1/NO%2028%20Fully%20Automatic%20Detections%20of%20Abnormalities%20of%20Brain%20MR%28abstract%29.pdf Arshad, Javed and Wang, Yin Chai and Abdulhameed, Rakan Alenezi and Narayanan, Kulathu Ramaiyer (2015) Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators. Applied Mathematics & Information Sciences, 9 (1). pp. 213-222. ISSN 1935-0090 https://www.scopus.com/record/display.uri?eid=2-s2.0-84907228666&origin=inward&txGid=0 DOI: 10.12785/amis/010127 |
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TR Photography Arshad, Javed Wang, Yin Chai Abdulhameed, Rakan Alenezi Narayanan, Kulathu Ramaiyer Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators |
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Image segmentation refers to the process of partitioning a digital image into multiple sets of pixels are known as segments. The main goal of image segmentation is to change and simplify the representation of an image into something that is more meaningful and easier to analyze. The manual transactions for segmentation by experts is a difficult phenomena and time consuming process as well as. Most of the images in the process received are lacking of good quality. The main objective of this study is to develop a
reliable mechanism to enhance the image quality and extract the abnormal portion through brain MR image accurately. A spatial filter
is designed by utilizing the spatial information of the image and further to use collective information to enhance the poor quality of
image(s), whereas, k-means clustering and mathematical morphological operations which extract the tumor segment from images. The proposed method is applied on different types of brain MR images for both visual and quantitative evaluations. Experimental results concluded during the practicum showed promising and reliable accuracy to open a thorough research for better future perspective of the technique developed in the article.
Fully Automatic Detections of Abnormalities of Brain MR Images by utilizing Spatial Information and Mathematical Morphological Operators. Available from: http://www.researchgate.net/publication/265294217_Fully_Automatic_Detections_of_Abnormalities_of_Brain_MR_Images_by_utilizing_Spatial_Information_and_Mathematical_Morphological_Operators [accessed Nov 3, 2015]. |
format |
Article |
author |
Arshad, Javed Wang, Yin Chai Abdulhameed, Rakan Alenezi Narayanan, Kulathu Ramaiyer |
author_facet |
Arshad, Javed Wang, Yin Chai Abdulhameed, Rakan Alenezi Narayanan, Kulathu Ramaiyer |
author_sort |
Arshad, Javed |
title |
Fully Automatic Detections of Abnormalities of Brain MR
Images by utilizing Spatial Information and Mathematical
Morphological Operators |
title_short |
Fully Automatic Detections of Abnormalities of Brain MR
Images by utilizing Spatial Information and Mathematical
Morphological Operators |
title_full |
Fully Automatic Detections of Abnormalities of Brain MR
Images by utilizing Spatial Information and Mathematical
Morphological Operators |
title_fullStr |
Fully Automatic Detections of Abnormalities of Brain MR
Images by utilizing Spatial Information and Mathematical
Morphological Operators |
title_full_unstemmed |
Fully Automatic Detections of Abnormalities of Brain MR
Images by utilizing Spatial Information and Mathematical
Morphological Operators |
title_sort |
fully automatic detections of abnormalities of brain mr
images by utilizing spatial information and mathematical
morphological operators |
publisher |
Natural Sciences Publishing Cor. |
publishDate |
2015 |
url |
http://ir.unimas.my/id/eprint/9315/1/NO%2028%20Fully%20Automatic%20Detections%20of%20Abnormalities%20of%20Brain%20MR%28abstract%29.pdf http://ir.unimas.my/id/eprint/9315/ https://www.scopus.com/record/display.uri?eid=2-s2.0-84907228666&origin=inward&txGid=0 |
_version_ |
1767209795385819136 |
score |
13.188404 |