An efficient and high quality medical CT image enhancement algorithm

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.

Section: Experiments Results and Analysismentioning

confidence: 99%

Section: Frequency Enhancement Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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Lei

et al. 2022

An optimized morphology transform‐based diagnostic computed tomography image enhancement using edge map

Rao

Bansal

Kaur

2022

In computed tomography (CT) imaging, a low‐quality CT scan, or more precisely, a noisy low contrast CT image, may provide insufficient information for visual interpretation of impacted regions. The purpose of this article is to propose a novel optimal morphological transform‐based method for improving the noise and contrast of CT images in the wavelet domain. The low‐quality CT image is first transformed using the optimized morphology transform method, which determines the optimal value of the parameter included in the fundamental morphology transformation equation. The parameter's optimum value is determined using the particle swarm optimization (PSO) method. The proposed approach then employs the discrete wavelet transform (DWT) to decompose the input and transformed CT image into higher and lower sub‐bands. Following that, the lower sub‐bands are modified by applying the correction factor via singular value decomposition (SVD). The input image's higher sub‐band (HH) is denoised using an edge map (EM)‐based method. This produces enhanced CT images with modified lower and higher sub‐band. Finally, the modified sub‐bands and remaining unprocessed higher frequency sub‐bands are processed using the inverse discrete wavelet transform (IDWT), resulting in an enhanced CT image. Experiments and validations are conducted on a CT images obtained from a publicly available database to assess the proposed scheme's effectiveness qualitatively and quantitatively. Significant quantitative study shows that the proposed approach surpasses existing image enhancement approaches in terms of signal‐to‐noise ratio, discrete entropy, enhancement measurement, and contrast ratio. The proposed method generates higher‐quality CT images, which is advantageous for disease inspection and diagnosis.

A powerful probabilistic model for noise analysis in medical images

Gürman

Bilgehan²,

Sabuncu³

et al. 2022

The statistical properties in various medical images demonstrate uncorrelated noise fluctuations. The signal noise fluctuations are generally due to physical imaging processes and have nothing to do with the tissue textures. Adding the noise types (e.g., quantization, electronics, photon) usually degrade medical images. The noise variation is usually assumed to be additive with zero-mean, constant variance Gaussian distribution. However, close consideration of different medical images indicates the need for better model representation to minimise the noise that can be vital in decision-making. This research proposed a probabilistic method to represent all real-type noise in general medical images. The method aims to cover most classical statistical models such as Gaussian, lognormal, Rayleigh, Weibull, and Nakagami without a prior examination to test for fitness. The proposed model was applied to actual clinical images to test the performance of the noise originating from the physical processes. The noise is assumed to be additive white Gaussian type with a zero mean and constant variance. The theoretical literature indicates that a nonlinear function can better represent noise. This research helps to form a relationship between the image intensity and the noise variance that yields the fitting parameters in the introduced nonlinear function. The validity of the proposed method was proved mathematically and tested using the well know Kolmogorov-Smirnov (K-S) and Akaike Information Criteria (AIC) tests. The method was successfully applied to various clinical images such as magnetic resonance, x-ray, and panoramic images. The model's performance is compared with the classical models using root mean squared error (RMSE), relative error (RE), and R 2 as the evaluation matrices. The presented model has outperformed all classic models.