A non-uniform quantization scheme for visualization of CT images

Anam Mehmood; Ishtiaq Rasool Khan; Hassan Dawood; Hussain Dawood; Anam Mehmood; Ishtiaq Rasool Khan; Hassan Dawood; Hussain Dawood

doi:10.3934/mbe.2021216

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4311-4326. doi: 10.3934/mbe.2021216

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A non-uniform quantization scheme for visualization of CT images

1.
Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
2.
Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan
3.
Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

Received: 14 March 2021 Accepted: 29 April 2021 Published: 18 May 2021

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.
- Clustering algorithm,
- computed tomography,
- high dynamic range,
- tone-mapping,
- medical image enhancement
Citation: Anam Mehmood, Ishtiaq Rasool Khan, Hassan Dawood, Hussain Dawood. A non-uniform quantization scheme for visualization of CT images[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4311-4326. doi: 10.3934/mbe.2021216

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Abstract

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.

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