PCRTAM-Net:A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background.At the same time,automated models struggle to capture representative and discriminative retinal vascular features.To fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network.In addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies.We evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and HRF.Our model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.

SWM-DE:Statistical wavelet model for joint denoising and enhancement for multimodal medical images

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.