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

A novel augmented reality for hidden organs visualisation in surgery : enhanced super-pixel with sub sampling and variance adaptive algorithm

In recent years, Augmented Reality (AR) has gained more attention as an effective tool in medical surgeries. The potentials of using AR in the medical field can change conventional medical procedures. However, the technology still facing fundamental challenges, especially hidden organs, for example, the organs behind the bowel and liver. The surgeries in these areas lack accuracy in the visualization of the soft tissues behind the bowel and liver like the uterus and gall bladder. This research aims to improve the accuracy of visualisation and the processing time of the augmented video. The proposed system consists of an enhanced super-pixel algorithm with variance weight adaptation and subsampling method. The simulation studies show significant improvements in visualization accuracy and a reduction in processing time. The results show reduced visualisation error by 0.23 mm. It provides better accuracy of the video in terms of visualization error from 1.58 ~ 1.83 mm to 1.35 ~ 1.60 mm, and the processing time decreases from 50 ~ 58 ms/frames to 40 ~ 48 ms/frames. The proposed system focused on the pixel refinement for the 3d reconstruction of the soft tissue, which helps solve the issue of visualising the bowel and liver in an augmented video

Enhanced classification loss functions and regularization loss function (ECLFaRLF) algorithm for bowel cancer feature classification

Bowel cancer is one of the most common cancers as stated in the bowel cancer cases statistics. The proposed technique is to recognize the pattern of tissue affected by bowel cancer by using support vector machine (SVM) classification. This research aims to increase the accuracy of detecting and classifying bowel cancer with reduced processing time. The proposed method considered a feature extraction and image classification by Eenhanced Classification Loss Functions and Regularization Loss Function (ECLFaRLF) algorithm. This method allowed for more precise interpretations regarding the best associations for bowel cancer. The proposed was tested on colorectal images from different datasets commonly investigated in the proposed solution. The test was evaluated by applying 10-fold cross-validation method. All classification methods provide differentiation rate above processing time 0.413 s, and accuracy 95.67% for the state of the art solution, but by introducing SVM2 classification algorithm produce high accuracy rate with average accuracy is 97.02% over 95.67% and with processing time 0.359 s over 0.413 s. This reality shows the significance of the discriminating power of the SVM2 classifier. The proposed framework has presented an examination of feature extraction and classification techniques to help pathologists in the identifying of benign and malignant diagnosis of bowel cancer

Augmented reality navigation for liver surgery : an enhanced coherent point drift algorithm based hybrid optimization scheme

Augmented reality (AR) based bowel or liver surgery still has not been implemented successfully due to limitations of accurate and proper image registration of uterus and gallbladder during surgery. This research aims to improve target registration error, which helps to navigate through hidden uterus and gallbladder during surgery. Therefore, it will reduce risk of cutting uterus or common bile duct during surgery, which can be fatal and cause devastating effects on the patient. The proposed system integrates the enhanced Coherent Point Drift (CPD) Algorithm with hybrid optimization scheme that incorporates Nelder-Mead simplex and genetic algorithm, to optimize the obtained weight parameter, which in turns improves the target image registration error and processing time of image registration. The system has minimized the target registration error by 0.31 mm in average. It provides a substantial accuracy in terms of target registration error, where the root mean square error is enhanced from 1.28 ± 0.68 mm to 0.97 ± 0.41 mm and improves processing time from 16 ~ 18 ms/frame to 11 ~ 12 ms/frame. The proposed system is focused on improving the accuracy of deformable image registration accuracy of soft tissues and hidden organs, which then helps in proper navigation and localization of the uterus hidden behind bowel and gallbladder hidden behind liver

A novel enhanced energy function using augmented reality for a bowel : modified region and weighted factor

The popularity of augmented reality in medical application is rising exponentially over time, especially in the medical sector. It possesses a greater possibility in the reduction of surgical risks by raising visual awareness during the operation. Incorrect representation or inadequate detail on target region or delay in processing time may result in serious consequences. Also, the absence of a proper mechanism to handle occlusions like nerves, vessels or medical equipment may affect the performance. Therefore, this research aims to improve the visualization accuracy of bowel region and reduce the processing time. a novel enhanced energy function using augmented reality for bowel is proposed. The proposed system targets the modified region and weighted factor that encompasses the power of the combined region and dense cue with longterm and accurate augmented reality display mechanism. The system is capable of providing detailed visual output by precisely placing the model developed using the CT images of the target object, over the live video. Also, by applying the least square approach, the system is capable of addressing larger deformation and occlusion that appears during the surgical procedure providing the most accurate display. The feature tracking and tracking recovery components help the entire visualization procedure to stay on track by automatically registering and re-registering the surface whenever required. The proposed system capable of running image registration without human involvement and it can even decide when to trigger the reregistration process whenever required. The results from the proposed system has minimized the overlay error by a larger number. We validated the system with different sets of samples from endoscopy. The dataset included the samples from the bowel region from people with three different age groups. The overlay error accuracy was 0.24777px, and the performance was 44fps. The proposed system is concentrated on the overlay accuracy and the processing time. This study has addressed the shortcoming of the previous systems regarding manual registration and rigid assumptions

A novel enhanced hybrid recursive algorithm: Image processing based augmented reality for gallbladder and uterus visualisation

Background: Current Augmented Reality systems in liver and bowel surgeries, are not accurate enough to classify the hidden parts such as gallbladder and uterus which are behind the liver and bowel. Therefore, we aimed to improve the visualization accuracy of bowel and liver augmented videos to avoid the unexpected cuttings on the hidden parts. Methodology: The proposed system consists of an Enhanced hybrid recursive matching and λ-parameterization techniques to improve the visualization. In addition, Mean Shift Filter is also added to improve the matching process while image registration. Results: Results proved that, the accuracy is improved in terms of liver and bowel surgeries Visualization errors about 0.53 mm and 0.22 mm respectively. Similarly, it can produce 2 more frames/sec compared to the current system. Conclusion: The proposed system worked towards the visualization of gallbladder and uterus while liver and bowel surgeries. So, this study solved the visualization issues, which are caused by neighbouring and hidden parts

A novel optimized initial cluster center and enhanced objective function : medical diagnosis through classification

Medical diagnosis through classification is often critical as the medical datasets are multilabel in nature, that is, a patient may have more than one health condition: high blood pressure, obesity, and diabetes. The aim of this article is to improve the accuracy and performance of multilabel classification using multilabel feature selection and improved overlapping clustering method. The proposed system consists of Optimized Initial Cluster Centers and Enhanced Objective Function technique to reduce the number of iterations in the clustering process thereby improving the clustering performance and to improve the clustering accuracy which will result in improving the accuracy and performance of multilabel classification. Ratios of clustering distance to class distance and execution time are used as the evaluation metric for accuracy and total execution time is used as the evaluation metric for performance. Based on the different combination with the number of labels, attributes, instances, and number of clusters, different values of accuracy and performance are obtained. The results on all 10 datasets show that the proposed technique is superior to the current technique. Furthermore, on average, the proposed technique has improved the classification accuracy by 5%–7%. Furthermore, the performance of new technique is improved by decreasing the processing time by 0.5–1 s on average. The proposed system targets on improving the accuracy and performance of the multilabel classification for medical diagnosis, which consists of multilabel feature selection and enhanced overlapping clustering technique. This study provides an acceptable range of accuracy with improved processing time, which assists the doctors in medical diagnosis (high blood pressure, obesity, and diabetes) of patients

A novel secure solution of using mixed reality in data transmission for bowel and jaw surgical telepresence: enhanced rivest cipher RC6 block cipher

Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance

Deep learning for liver tumour classification : enhanced loss function

Background and Aim: deep learning has not been successfully implemented in liver tumour feature extraction and classification using computer-aided diagnosis. This study aims to enhance classification accuracy and improves the processing time to better differentiate tumour types. Methodology: This study proposed a hybrid model, which combines the regularization function with the current loss function for the support vector machine (SVM) classifier. Regularization function is used for prioritizing image classes before feeding it to the linear mapping. The proposed model consists of the region growing algorithm to get the region-of-interest (ROI), and Weiner filtering algorithm for image enhancement and noise removal. The gray level co-occurrence matrix (GLCM) was performed to extract the feature from the image. The extracted feature then fed to SVM classifier using selected feature vectors to classify the affected region and neglecting the unwanted areas. Results: classification accuracy was calculated using probability score, and the processing time was calculated based on the total execution time. The proposed system was able to achieve an average classification accuracy of 98.9%, which is about 2–3% higher than the current system. The results showed that 12 ms reduced the processing time on average. Conclusion: The proposed system focused on improving feature extraction and classification for different types of tumours from the MRI images. The study solved the problem in linear mapping of support vector machine and enhanced the classification accuracy and the processing time of early diagnosis of three different types of tumours in liver MRI images