An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology

Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses.

Automated summary

This paper presents a new method for enhancing non-contrast CT images using dual-tree complex wavelet transform and adaptable morphology. The proposed method outperforms existing techniques in terms of image quality and can be useful for detecting and diagnosing illnesses.