A non-uniform quantization scheme for visualization of CT images
Medical science heavily depends on image acquisition and post-processing for accurate diagnosis and treatment planning. The introduction of noise degrades the visual quality of the medical images during the capturing process, which may result in false perception. Therefore, medical image enhancement...
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doaj-ff539e3b75764636b56ef2e9ab15b0842021-06-10T02:03:52ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-05-011844311432610.3934/mbe.2021216A non-uniform quantization scheme for visualization of CT imagesAnam Mehmood 0Ishtiaq Rasool Khan 1 Hassan Dawood 2Hussain Dawood 32. Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan1. Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia2. Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan3. Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaMedical science heavily depends on image acquisition and post-processing for accurate diagnosis and treatment planning. The introduction of noise degrades the visual quality of the medical images during the capturing process, which may result in false perception. Therefore, medical image enhancement is an essential topic of research for the improvement of image quality. In this paper, a clustering-based contrast enhancement technique is presented for computed tomography (CT) images. Our approach uses the recursive splitting of data into clusters targeting the maximum error reduction in each cluster. This leads to grouping similar pixels in every cluster, maximizing inter-cluster and minimizing intra-cluster similarities. A suitable number of clusters can be chosen to represent high precision data with the desired bit-depth. We use 256 clusters to convert 16-bit CT scans to 8-bit images suitable for visualization on standard low dynamic range displays. We compare our method with several existing contrast enhancement algorithms and show that the proposed technique provides better results in terms of execution efficiency and quality of enhanced images.https://www.aimspress.com/article/doi/10.3934/mbe.2021216?viewType=HTMLclustering algorithmcomputed tomographyhigh dynamic rangetone-mappingmedical image enhancement |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anam Mehmood Ishtiaq Rasool Khan Hassan Dawood Hussain Dawood |
spellingShingle |
Anam Mehmood Ishtiaq Rasool Khan Hassan Dawood Hussain Dawood A non-uniform quantization scheme for visualization of CT images Mathematical Biosciences and Engineering clustering algorithm computed tomography high dynamic range tone-mapping medical image enhancement |
author_facet |
Anam Mehmood Ishtiaq Rasool Khan Hassan Dawood Hussain Dawood |
author_sort |
Anam Mehmood |
title |
A non-uniform quantization scheme for visualization of CT images |
title_short |
A non-uniform quantization scheme for visualization of CT images |
title_full |
A non-uniform quantization scheme for visualization of CT images |
title_fullStr |
A non-uniform quantization scheme for visualization of CT images |
title_full_unstemmed |
A non-uniform quantization scheme for visualization of CT images |
title_sort |
non-uniform quantization scheme for visualization of ct images |
publisher |
AIMS Press |
series |
Mathematical Biosciences and Engineering |
issn |
1551-0018 |
publishDate |
2021-05-01 |
description |
Medical science heavily depends on image acquisition and post-processing for accurate diagnosis and treatment planning. The introduction of noise degrades the visual quality of the medical images during the capturing process, which may result in false perception. Therefore, medical image enhancement is an essential topic of research for the improvement of image quality. In this paper, a clustering-based contrast enhancement technique is presented for computed tomography (CT) images. Our approach uses the recursive splitting of data into clusters targeting the maximum error reduction in each cluster. This leads to grouping similar pixels in every cluster, maximizing inter-cluster and minimizing intra-cluster similarities. A suitable number of clusters can be chosen to represent high precision data with the desired bit-depth. We use 256 clusters to convert 16-bit CT scans to 8-bit images suitable for visualization on standard low dynamic range displays. We compare our method with several existing contrast enhancement algorithms and show that the proposed technique provides better results in terms of execution efficiency and quality of enhanced images. |
topic |
clustering algorithm computed tomography high dynamic range tone-mapping medical image enhancement |
url |
https://www.aimspress.com/article/doi/10.3934/mbe.2021216?viewType=HTML |
work_keys_str_mv |
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