Robust multi-modal COVID-19 medical image registration using dense deep learning descriptor model

Abstract

In medical image processing, multi-modal medical image registration is a challenging task due to the varied image characteristics. Because of the Non-functional strength relation and the erratic intricate deformations among images. To overcome these issues, this paper proposed an enhanced residual dense learning data descriptor for multi-modal COVID-19 image registration. In this work, input images are taken from the COVID-19 X-ray and CT Chest Images Dataset. Initially, the input images are pre-processed using the boosted switching bilateral filter (BSBF), in which the best median value is examined using a Sorted Quadrant Median Vector (SQMV). Then, the Directed Edge Enhancer (DEE) algorithm is used for the edge enhancement process. These pre-processed images are provided as the input of a deep learning based multi-scale feature extraction module to diminish the mutual interference of features and make it easier to train the network model. Data Adaptive Descriptor (DAD) is provided for structural representation, and the self-similarity metrics of the reference and floating images are examined by the Sum of Squared Differences (SSD). The goal function for image registration is made to the final deformation field based on SSD. Here, the simulation is performed by using a Python tool. The accuracy value of the proposed method in the COVID-19 X-ray and CT Chest images dataset is 96%, and the MSE value is 0.03%. Compared with other existing methods, our proposed method produces better performance. The proposed model is more efficient by using the hybrid deep learning methodology.