Lung CT image enhancement based on total variational frame and wavelet transform

Abstract

Benefitting from the development of computer vision, computed tomography (CT) images have been used for assisting doctor's clinical diagnosis and improving the diagnostic efficiency. However, there exist some issues in medical images, such as low contrast, obscure detail, and complex noise due to the restriction of the system and the equipment in the process of imaging, medical images. To resolve these issues, a novel lung CT image enhancement method based on total variational framework combined with wavelet transform is proposed. Firstly, low‐frequency structure layer with low contrast and high‐frequency details layer with complex noise signals are acquired by decomposing the original image using total variational framework. Then, through the analysis of the histogram distribution characteristics of CT image, structure layer requires contrast enhancement; at the same time, the detail layer performs wavelet transform adaptive threshold denoising to remove noise. Finally, weight fusion of processed structure layer and details layer is performed to obtain the final fusion enhancement CT images. Experimental results show that the proposed method can enhance the contrast of clinical lung CT images, improve the clarity of details, and effectively suppress artifacts and complex noises. Contrast and sharpness—objective indicators—prove the proposed method's advantages. Subjectively, the proposed method performs superior over other existing CT enhancement methods, which achieves a better visual recognition.

An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

“…To suppress the artificial artefacts that are introduced by the contrast enhancement technique and original image artefacts, the proposed method adopts a regularized denoising algorithm consisting of total variational (TV) and wavelet threshold denoising technology [22]. The TV model decomposes the input image into a structure layer and a detail layer by minimizing an energy function defined as follows: $$$$\begin{equation}\min E(u) = \frac{\lambda }{2}\int_{\Omega }{{{{(u - {u}_0)}}^2dxdy + \int_{\Omega }{{\sqrt {{{\left| {\nabla u} \right|}}^2} dxdy}}}}\end{equation}$$$$…”

“…To suppress the artificial artefacts that are introduced by the contrast enhancement technique and original image artefacts, the proposed method adopts a regularized denoising algorithm consisting of total variational (TV) and wavelet threshold denoising technology [22]. The TV model decomposes the input image into a structure layer and a detail layer by minimizing an energy function defined as follows:…”

Medical Image Fusion

“…Reducing visual distortions after combining panchromatic (PAN), hyperspectral (HS), and multi-spectral (MS) images is the primary problem in the field of remote sensing. This paper [11], discuss a novel method based on wavelet transform and total variational framework is presented for enhancing lung CT images. To generate the low-frequency structure layer with low contrast and the high-frequency details layer with complex noise signals, the original image is first deconstructed using a total variational framework.…”