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A non-uniform quantization scheme for visualization of CT images.

Anam Mehmood¹, Ishtiaq Rasool Khan², Hassan Dawood¹

¹Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan.

Mathematical Biosciences and Engineering : MBE

|July 2, 2021

Summary

This study introduces a novel clustering-based contrast enhancement technique for computed tomography (CT) images. The method improves image quality and execution efficiency for better medical diagnosis.

Keywords:

Clustering algorithm computed tomography high dynamic range medical image enhancement tone-mapping

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Area of Science:

Medical Imaging
Image Processing
Radiology

Background:

Medical image quality is crucial for diagnosis and treatment planning.
Image noise degrades visual quality, potentially leading to misinterpretations.
Medical image enhancement is vital for improving diagnostic accuracy.

Purpose of the Study:

To present a novel clustering-based contrast enhancement technique for computed tomography (CT) images.
To improve the visual quality and diagnostic utility of CT scans.
To develop an efficient method for converting high-bit-depth CT images for standard displays.

Main Methods:

A recursive data splitting into clusters is employed to maximize error reduction.
The technique groups similar pixels, maximizing inter-cluster and minimizing intra-cluster similarities.
256 clusters are used to convert 16-bit CT scans to 8-bit images for visualization.

Main Results:

The proposed method effectively enhances contrast in CT images.
It demonstrates superior execution efficiency compared to existing algorithms.
Enhanced images show improved quality suitable for standard displays.

Conclusions:

The clustering-based contrast enhancement technique offers a valuable tool for medical image processing.
The method provides an efficient and effective solution for CT image enhancement.
This approach aids in improving diagnostic accuracy by enhancing image quality.