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

Abstract

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.

Design of a high efficiency cross-domain medical image retrieval system using augmented transfer learning

“…These features can be further used in the field of medical image retrieval, for extracting multiple types of images from the database. Special purpose networks for medical image retrieval are proposed in [14,15,16], wherein researchers have discussed the use of multiple modality Siamese Networks, convolution feature segmentation with Morphological filtering, and jointly combining deep and handcrafted visual features (DHV). These models are capable of reducing delay needed for retrieval via reducing redundancies during ranking calculations, thereby improving overall precision, recall, and accuracy performance.…”

Dimensions of Internet of Things: Technological Taxonomy Architecture Applications and Open Challenges—A Systematic Review

“…Yao et al [62] explained the limitations of the manual labeling of traditional image matching technology. For solving this problem, a content-based medical image retrieval technique was used.…”

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

“…Another application of IoT in conjunction with segmentation techniques is in the continuous monitoring of diseases, to measure their progression or regression. Aware of this application, Yao et al [ 53 ] developed an IoT-based technique, along with fuzzy clustering techniques, Otsu thresholding, and morphological operations for the segmentation and monitoring of lung cancer in CT images, to make the prevention and monitoring of the disease progression easier and more efficient.…”