Estimation of the surface topography from monocular endoscopic images

Oda, Norihiko; Hasegawa, Jun; Nonami, Tetsuo; Yamaguchi, Masahiro; Ohyama, Nagaaki

doi:10.1016/0030-4018(94)90682-3

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…Correlation-based tracking has been proposed [13], [32], [33], [31], [42], but has been limited by the lack of sensitivity to poor texturization. B-spline polynomial fitting [18], homography transformation [2], and radial basis functions [36], [38]- [40], [35] have been shown to be effective in capturing the low-order deformations of a small region of the beating heart at real-time speeds (50 Hz); however, higher order-deformation tracking using these methods becomes nontrivial and computationally expensive.…”

Section: B Prior Work On Endoscopic Tissue Tracking and Registrationmentioning

confidence: 99%

Tissue Tracking and Registration for Image-Guided Surgery

Yip

Lowe

Salcudean

et al. 2012

IEEE Trans. Med. Imaging

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Vision-based tracking of tissue is a key component to enable augmented reality during a surgical operation. Conven- tional tracking techniques in computer vision rely on identifying strong edge features or distinctive textures in a well-lit environ- ment; however endoscopic tissue images do not have strong edge features, are poorly lit and exhibit a high degree of specular reflection. Therefore, prior work in achieving densely populated 3D features for describing tissue surface profiles require complex image processing techniques and have been limited in providing stable, long-term tracking or real-time processing. In this paper, we present an integrated framework for ac- curately tracking tissue in surgical stereo-cameras at real-time speeds. We use a combination of the STAR feature detector and Binary Robust Independent Elementary Features to acquire salient features that can be persistently tracked at high frame rates. The features are then used to acquire a densely-populated map of the deformations of tissue surface in 3D. We evaluate the method against popular feature algorithms in in-vivo animal study video sequences, and we also apply the proposed method to human partial nephrectomy video sequences. We extend the salient feature framework to support region tracking in order to maintain the spatial correspondence of a tracked region of tissue or a medical image registration to the surrounding tissue. In-vitro tissue studies show registration accuracies of 1.3-3.3 mm using a rigid-body transformation method.

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Section: B Prior Work On Endoscopic Tissue Tracking and Registrationmentioning

confidence: 99%

Tissue Tracking and Registration for Image-Guided Surgery

Yip

Lowe

Salcudean

et al. 2012

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…(1) In the following, we develop a method to determine the scale factor m. 1) Determine point pairs in two images that correspond to the same object points.…”

Section: Determining the Relative Shapementioning

confidence: 99%