Head CT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs

With the rapid application of medical imaging technology, the number of medical images is increasing and the form is gradually diversified. The management and retrieval of medical images is an urgent problem to solve. Traditional image matching technology has many limitations due to the complexity of its manual labeling. Therefore, content-based medical image retrieval is a new method to solve this problem. The technique uses the visual attributes contained in the image to establish the feature index of the image, adopts Sparse Connectivity and weight sharing of CNN (Convolutional Neural Network) to obtain the image feature, and then perform feature similarity matching between the query image and the image in the image database, and implement retrieval according to the matching result. The intensive matching based on the Internet of Things has great potential in the application of medical image 3D reconstruction. Compared with the traditional algorithm based on convolution feature segmentation and morphological filtering, the biggest advantage of the proposed algorithm is the computational efficiency, aiming at the same image. The efficiency of dense matching based on the Internet of Things has greatly improved. At the same time, the problem of occlusion, moving objects, rotation, and scaling in multi-view images is well handled. Experiments show that the proposed method can extract more dense and reliable matching points for multi-view images, which is beneficial to the subsequent 3D reconstruction.