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

Ganesh Shrestha, Abeer Alsadoon, P. W.C. Prasad, Thair Al-Dala’in, Ahmad Alrubaie

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)17893-17922
Number of pages30
JournalMultimedia Tools and Applications
Issue number12
Publication statusPublished - May 2021


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