A Novel Solution of Using Mixed Reality in Bowel and Oral and Maxillofacial Surgical Telepresence: 3D Mean Value Cloning algorithm

Background and aim: Most of the Mixed Reality models used in the surgical telepresence are suffering from discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and the virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. Methodology: The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. Results: As compared to the state of the art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds. Conclusion: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.Comment: 27 page

Augmented reality for dental implant surgery : enhanced ICP

Augmented reality surgery has not been successfully implemented in dental implant surgery due to the negative impact of an incorrect implant placement. This research aimed to improve the convergence between computed tomography derived teeth model and real-time stereo view of patient’s teeth to provide high registration accuracy. Enhanced iterative closest point algorithm is proposed to reduce the error caused due to matching wrong points. Weighting mechanism and median value are used to reduce alignment error caused due to matching wrong points. In addition, random sample consensus (RANSAC) algorithm is used to detect and remove the outlier. Furthermore, the current solution for dental implants did not provide the position and orientation of the surgical tool, and without this information, there is a risk of damaging adjacent structure, dental nerves, and root canals. Optical tracking device is used in the proposed solution to address this information and ensure that nerve does not get damaged during the dental implant placement surgery. While the state-of-the-art solution provided 0.44 mm registration accuracy, the proposed solution was improving it by providing 0.33 mm registration accuracy. Additionally, the proposed system can produce good results despite not having a good initialization. The processing time improved to 14 fps in comparison to the 9-fps given by state-of-the-art solution. The proposed system improved the accuracy of convergence and the processing time compared to the globally optimal-ICP algorithm. We also employed RANSAC algorithm to detect and remove the outlier on the estimation and reduce the influence of extreme points

Comparative analysis : accurate prediction to the future stock prices

Accurate stock price prediction has an increasingly prominent role in a market where rewards and risks fluctuate wildly. Market control is a technique utilized by brokers to adjust the cost of budgetary resources. Recently there has been a significant increase in the use of artificial intelligence techniques in stock markets. Reinforcement learning has become particularly important in stock market forecasting. There is a need for modern techniques to improve share analysis and to detect unfair trading. Due to the high volatility and non-stationary nature of the stock market, forecasting the trend of financial time series remains a big challenge. This research explores, compares, and analyses the different artificial intelligent techniques used in predicting stock prices. The aim of this research is to give a comparative analysis to understand how to detect and analyze unfair trading and to detect price manipulation. Also, our aim is to explain how reinforcement deep learning could avoid and analyze the risks and unfair trading in stock market. The result of this study addresses current challenges of reducing the unfair trading across the stock. A successful and accurate prediction to the future stock prices ultimately results in profit maximization. Such prediction is important for many individuals including companies, traders, market participants, and data analysts. In conclusion, Reinforcement learning in the stock market is in its early development and a lot more research is needed to make it a reliable method in this field

A recent review and a taxonomy for hard and soft tissue visualization-based mixed reality

Background: Mixed reality (MR) visualization is gaining popularity in image-guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments. Some of these factors include: the end users are not considered, the limitations in the operating room, and the medical images are not fully unified into the operating interventions. Methodology: The purpose of this article is to use Data, Visualization processing, and View (DVV) taxonomy to evaluate the current MR systems. DVV includes all the components required to be considered and validated for the MR used in hard and soft tissue surgeries. This taxonomy helps the developers and end users like researchers and surgeons to enhance MR system for the surgical field. Results: We evaluated, validated, and verified the taxonomy based on system comparison, completeness, and acceptance criteria. Around 24 state-of-the-art solutions that are picked relate to MR visualization, which is then used to demonstrate and validate this taxonomy. The results showed that most of the findings are evaluated and others are validated. Conclusion: The DVV taxonomy acts as a great resource for MR visualization in IGS. State-of-the-art solutions are classified, evaluated, validated, and verified to elaborate the process of MR visualization during surgery. The DVV taxonomy provides the benefits to the end users and future improvements in MR

Deep learning for size and microscope feature extraction and classification in oral cancer : enhanced convolution neural network

Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting