Medical images are usually degraded by numerous noises during acquisition or transmission,which often causes low contrast leading to deterioration of image quality.As such,medical image denoising and enhancement has b...Medical images are usually degraded by numerous noises during acquisition or transmission,which often causes low contrast leading to deterioration of image quality.As such,medical image denoising and enhancement has become a paramount routine task.To overcome this problem,we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation.Firstly,we propose a statistical model in formulating the marginal distribution of the wavelet coefficients.This model is integrated into a Bayesian inference framework to develop a maximum a posterior(MAP)estimator of the noise-free coefficient.Based on the statistical model,we eliminate the need for noise level estimation,and allows the model to automatically adapts to the observed image data.Secondly,we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images,while preserving the image details.After these operations,the image is decomposed into several wavelet subbands to extract the illumination and detail components.The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04,48.81,47.81,and 45.75 for OCTA,FFA,CT,and X-ray imaging modalities,and performs better than the state-of-the-art methods.The proposed algorithm can effectively and efficiently enhance medical images,which will assist the clinicians in disease diagnosis,monitoring,and treatment.展开更多
基金This work was supported by the National Natural Science Foundation of China(62250410370)National Science and Technology Funding for Foreign Youth Talent Program(QN2022033002 L)+2 种基金Guangxi Natural Science Foundation for Youth Science and Technology(2021GXNSFBA220075)a grant from the Guangxi Postdoctoral Special Support Fund(C21RSC90ZN02 and C22RSC90ZN01)Scientific Research Fund(YXRSZN03 and UF20035Y).
文摘Medical images are usually degraded by numerous noises during acquisition or transmission,which often causes low contrast leading to deterioration of image quality.As such,medical image denoising and enhancement has become a paramount routine task.To overcome this problem,we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation.Firstly,we propose a statistical model in formulating the marginal distribution of the wavelet coefficients.This model is integrated into a Bayesian inference framework to develop a maximum a posterior(MAP)estimator of the noise-free coefficient.Based on the statistical model,we eliminate the need for noise level estimation,and allows the model to automatically adapts to the observed image data.Secondly,we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images,while preserving the image details.After these operations,the image is decomposed into several wavelet subbands to extract the illumination and detail components.The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04,48.81,47.81,and 45.75 for OCTA,FFA,CT,and X-ray imaging modalities,and performs better than the state-of-the-art methods.The proposed algorithm can effectively and efficiently enhance medical images,which will assist the clinicians in disease diagnosis,monitoring,and treatment.
