Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico

Although the two dimensional Speckle Tracking Echocardiography has gained a strong position among medical diagnostic techniques in cardiology, it still requires further developments to improve its repeatability and reliability. Few works have attempted to incorporate the left ventricle segmentation results in the process of displacements and strain estimation to improve its performance. We proposed the use of mask information as an additional penalty in the elastic image registration based displacements estimation. This approach was studied using a short axis view synthetic echocardiographic data, segmented using an active contour method. The obtained masks were distorted to a different degree, using different methods to assess the influence of the segmentation quality on the displacements and strain estimation process. The results of displacements and circumferential strain estimations show, that even though the method is dependent on the mask quality, the potential loss in accuracy due to the poor segmentation quality is much lower than the potential accuracy gain in cases where the segmentation performs well.

A survey of unsupervised learning in medical image registration

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

Near Infrared and Visible Image Registration using Whale Optimization Algorithm

This paper reports the use of a nature-inspired metaheuristic algorithm known as ‘Whale Optimization Algorithm’ (WOA) for multimodal image registration. WOA is based on the hunting behaviour of Humpback whales and provides better exploration and exploitation of the search space with small possibility of trapping in local optima. Though WOA is used in various optimization problems, no detailed study is available for its use in image registration. For this study different sets of NIR and visible images are considered. The registration results are compared with the other state of the art image registration methods. The results show that WOA is a very competitive algorithm for NIR-visible image registration. With the advantages of better exploration of search space and local optima avoidance, the algorithm can be a suitable choice for multimodal image registration.

A survey of unsupervised learning in medical image registration

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.