Abstract
Background and objectivesSurgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments.MethodsThis paper presents a variational framework to track the motion of moving objects in surgery videos. The key contributions are as follows: (1) A denoising method using stochastic resonance in maximal overlap discrete wavelet transform is proposed and (2) a robust energy functional based on Bhattacharyya coefficient to match the target region in the first frame of the input sequence with the subsequent frames using a similarity metric is developed. A modified affine transformation-based registration is used to estimate the motion of the features following an active contour-based segmentation method to converge the contour resulted from the registration process.Results and conclusionThe proposed method has been implemented on publicly available databases; the results are found satisfactory. Overlap index (OI) is used to evaluate the tracking performance, and the maximum OI is found to be 76% and 88% on private data and public data sequences.
Highlights
Looking at the steep rise in cardiac diseases, bona fide treatment including surgery is necessary to prevent its rise and avoid sudden cardiac death [1]
The perceptual quality measurement (PQM) [41] is provided in Fig. 3, which shows greater value in case of maximal overlap discrete wavelet transform (MODWT) suggesting higher efficacy of MODWT; in this figure, m denotes the mass of the particle that moves under stochastic condition
To determine the quality of the denoised image, we have calculated distribution separation measure that estimates the degree of image quality
Summary
Looking at the steep rise in cardiac diseases, bona fide treatment including surgery is necessary to prevent its rise and avoid sudden cardiac death [1]. During robotic surgery or laparoscopic surgery, the surgeons concentrate on the surgery to avoid even slight, possible mortality and morbidity and usually get stressed. In this scenario, motion tracking of the tools and viewing the desired operating field may be considered two supportive pillars to augment the treatment and improve success rate. Motion tracking of the tools and viewing the desired operating field may be considered two supportive pillars to augment the treatment and improve success rate Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments
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