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AuthorDakua S.P.
AuthorAbinahed J.
AuthorZakaria A.
AuthorBalakrishnan S.
AuthorYounes G.
AuthorNavkar N.
AuthorAl-Ansari A.
AuthorZhai X.
AuthorBensaali F.
AuthorAmira A.
Available date2020-04-01T06:59:41Z
Publication Date2019
Publication NameInternational Journal of Computer Assisted Radiology and Surgery
ResourceScopus
ISSN18616410
URIhttp://dx.doi.org/10.1007/s11548-019-02030-z
URIhttp://hdl.handle.net/10576/13656
AbstractBackground and objectives: Surgical 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. Methods: This 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 conclusion: The 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. - 2019, The Author(s).
SponsorOpen Access funding provided by the Qatar National Library. This work was partly supported by NPRP Grant #NPRP 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherSpringer Verlag
SubjectBrain aneurysm clipping
Cerebral aneurysm
Heart surgery
Level sets
Object tracking
Segmentation
TitleMoving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping
TypeArticle
Pagination2165-2176
Issue Number12
Volume Number14
dc.accessType Open Access


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