Abstract— This paper introduces a stereoscopic fibroscope
imaging system for Minimally Invasive Surgery (MIS) and
examines the feasibility of utilizing images transmitted from the
distal fibroscope tip to a proximally mounted CCD camera to
recover both camera motion and 3D scene information. Fibre
image guides facilitate instrument miniaturization and have the
advantage of being more easily integrated with articulated
robotic instruments. In this paper, twin 10,000 pixel coherent
fibre bundles (590µm diameter) have been integrated into a
bespoke laparoscopic imaging instrument. Images captured by
the system have been used to build a 3D map of the
environment and reconstruct the laparoscope’s 3D pose and
motion using a SLAM algorithm. Detailed phantom validation
of the system demonstrates its practical value and potential for
flexible MIS instrument integration due to the small footprint
and flexible nature of the fibre image guides.
S the number of Minimally Invasive Surgical (MIS)
procedures performed with robotic assistance
multiplies, there is an increasing demand to improve the
functionality and usability of such systems to allow for more
complex procedures to be performed. Existing robotic
assisted MIS platforms, such as the daVinci surgical robot
(Intuitive Surgical, Sunnyvale, CA), allow a surgeon to
interact with the operative environment through a master-
slave architecture while viewing a magnified 3D
representation of the surgical scene. The provision of
immersive stereo vision has proved to be one of the major
strengths of the system when manipulating complex
Currently, one of the main focuses of MIS robot research
is in the design of flexible instruments that can follow
curved anatomical pathways with stereo vision, allowing
regional and global integration of the 3D surgical
environment. While traditional stereo-laparoscope systems,
similar to that utilised by the daVinci, are not compatible
with such an approach (due to the use of rigid, rod lens for
the optical systems), miniaturised coherent fibre-optic
bundles offer the advantages of flexibility and
miniaturization required for integration with articulated
instruments, but at a cost of decreased image resolution.
The purpose of this paper is to present a stereo imaging
instrument to evaluate the feasibility of using fibre bundles
for instrument localisation and soft tissue mapping within a
Manuscript received September 15, 2008.
D. Noonan, P. Mountney, D. Elson, A. Darzi, G-Z.Yang are with the
Institute of Biomedical Engineering , Dept. of Biosurgery & Surgical
Technology, Dept. of Computing, Imperial College London, London, SW7
2AZ, UK (e-mail: email@example.com).
sequential vision only SLAM (Simultaneous Localisation
and Mapping) system. Key technical issues associated with
developing the stereo fibroscope imaging system and its 3D
vision algorithms are presented. Such a system has the
potential to provide in situ 3D reconstruction required for
implementing advanced safety techniques, such as active
constraints and motion stabilisation. Results obtained from a
silicone tissue phantom and ex-vivo porcine tissue were
validated using optical tracking and a registered CT scan.
A. Robotic Assisted Minimally Invasive Surgery
In MIS, a miniaturised CCD or fibre optic camera is
commonly used to pass through a natural orifice or small
incision of the body to gain remote vision. Specialised
instruments are also inserted through additional incisions to
perform the actual surgical tasks. The use of small incisions
results in reduced patient trauma, blood loss and
hospitalisation costs , thus making it an attractive
alternative to open surgery. However, while procedures
completed in this manner offer several advantages, the
inherent technical difficulty is significantly higher as the
distal dexterity is severely impaired by the long, rigid
instruments and gross movements are subject to a fulcrum
effect at the trocar port , as is illustrated in Fig. 1.
Ergonomically, the visualisation provided is misaligned with
the motor axis and is often through a monoscopic display,
which lacks depth perception. This often leads to fatigue,
poor hand-eye coordination and increased surgical errors .
Clinically, several robotic platforms have been developed
to overcome these difficulties. The daVinci surgical robot,
for example, operates as a tele-manipulator, where the
surgeon controls miniaturized slave instruments on three or
four robotic arms via a master console . The system
successfully tackles some of the traditional difficulties
associated with MIS by providing stereoscopic visualisation,
an ergonomic seating position, improved distal dexterity,
motion scaling and tremor filtering at 6Hz.
In order to operate along curved anatomical pathways and
access regions which are not in a direct line of sight from the
incision point, there is currently increasing research interest
into the development of flexible or articulated robotic
systems. Example systems include the Highly Articulated
Robotic Probe (HARP) for epicardial atrial ablation , a
“snake” like robotic system designed to provide additional
dexterity at the instrument tip for Ear, Nose and Throat
(ENT) surgery , and a high-dexterity, modular instrument
for coronary artery bypass grafting . Systems with
alternative white light and fluorescence imaging  have
also been proposed. A natural extension of such systems is
A Stereoscopic Fibroscope for Camera Motion and 3D Depth
Recovery during Minimally Invasive Surgery
David P. Noonan, Peter Mountney, Daniel S. Elson, Ara Darzi and Guang-Zhong Yang
2009 IEEE International Conference on Robotics and Automation
Kobe International Conference Center
Kobe, Japan, May 12-17, 2009
978-1-4244-2789-5/09/$25.00 ©2009 IEEE 4463
Authorized licensed use limited to: Imperial College London. Downloaded on May 10,2010 at 10:57:23 UTC from IEEE Xplore. Restrictions apply.
the provision of camera position with simultaneous 3D scene
reconstruction through stereo vision so that advanced
functions such as adaptive motion stabilisation, augmented
reality, active constraints and dynamic view expansion can
be deployed   . The stereo fibre image guide
based system described in this paper is ideally placed for
integration with such flexible systems where miniaturization
is a key requirement.
B. Simultaneous Localization and Mapping (SLAM)
Estimating the position of a camera relative to its
environment and a 3D model of that environment is an
important and challenging problem in robotic vision. The
ability of SLAM to build long term maps and remain robust
to drift has led to the development of many systems using a
variety of hardware from ultrasound to laser range finders
and cameras. The majority of these systems have been
developed for mobile robots navigating in urban
environments, and the size of the hardware is not compatible
with robotic assisted surgery. It has been shown that optical
approaches can be used to recover 3D structure in MIS [10,
11]. Such approaches are non invasive and make use of
hardware which is already available during surgery.
However, these methods face a number of challenges due to
the complexity of the environment. 1) Features on the
surface of tissue may be sparse and change in appearance as
the anatomical feature may be below the surface. 2) Specular
highlights need to be detected and ignored, they may also
occlude features. 3) The lighting conditions can vary
significantly, changing the appearance of features. 4) Tissue
is not rigid and can deform as a result of respiration, cardiac
motion and tissue tool interaction.
The use of miniaturised fibre bundles introduces additional
challenges in the form of image resolution. Pixel count is
compromised for a reduction in bend radius of the fibre
bundles, thus leading to low quality images. Additionally,
SLAM is made more challenging due to the small baseline
between the stereo pair and the short working distance and
limited field-of-view of the GRIN lens, which is used to
focus the light into the bundles.
In , we demonstrated the principal that SLAM could
be used in MIS with high quality stereo cameras in a rigid
laparoscope. In , a monocular SLAM system is
presented for ENT surgery, however the environment
mapped is small and features appear to be in the scene the
entire time and no loops are closed. A system developed by
 is used to map larger areas however the approach relies
on the use of an Optotrak to track the laparoscope making
the assumption that the scope is rigid.
While the previous work utilized the high quality images
captured using the stereo laparoscope of the daVinci system,
an equivalent image resolution and field-of-view is not
currently available with flexible fibre image guides. As such
the system described in this paper was developed to identify
and overcome technical difficulties from mechanical,
calibration and software algorithm perspectives, in order to
evaluate the feasibility of accurate camera localisation and
Figure 1: Schematic illustration of a typical endoscope motion in-vivo.
II. EXPERIMENTAL SETUP
A. Mechanical & Optical System Design
The stereo video sequences used in this paper were
recorded using free-hand data acquisition with a custom
stereo fibroscope test-rig as shown in Fig. 2. The system was
designed to allow for the acquisition of stereo images using
fibre image guides and to facilitate the validation of the
algorithms which were then experimentally tested on the
Figure 2: Schematic illustration of the stereo fibroscope indicating the
location of 1) 3-axis joint to allow for free-hand camera motion, 2) Rigid
body to mount optical tracking markers to provide ground truth data for
camera motion validation, 3) Protective tubing for fibre bundles 4) 10,000
pixel coherent fibre image guide (x2) 5) Grub screw (x2) to adjust camera
vergence and 6) Tubing path to image acquisition system. The camera
baseline, b, of 3.8mm is also marked.
The system features stereo flexible, coherent fibre image
guides (Sumitomo IGN-05/10, 10,000 fibres, length 1.5 m,
diameter 0.59mm, min. bend radius 25mm) running down a
rigid shaft of diameter 10mm in a configuration similar to a
laparoscope. The fibres, (4) in Fig. 2, are housed in twin
protective polyurethane sheaths (3) which are clamped both
within the shaft and just prior to an optical mounting stage.
The fibres exit the sheaths and are clamped into place before
passing through twin adjustable distal tip mounting arms.
The separation of the arms (and thus the distance between
the fibres) can be adjusted using grub screws threaded
through the aluminium outer casing of the shaft (5). This
allows the baseline, b, and vergence of the stereo pair to be
adjusted as required. The baseline used during the
experiments described in this paper was 3.8mm. A graded
index (GRIN) lens (Grintech GmbH) is cemented onto the
end of each image guide (diameter 0.5mm, working distance
10mm, NA 0.5) to image an area of 35×35mm2 at a working
distance of 20mm onto the distal end of each image guide.
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The fibres are both clamped into a single fibre mount and
imaged onto a CCD camera (UEye, UI-2250-C/CM) using
an achromatic ×10 microscope objective and 100mm focal
length lens, as shown in Fig. 3.
Figure 3: Schematic illustration of the optical setup. The flexible image
guides are housed in a custom clamp attached to an XY positioning stage.
This allows for fine focussing of the images onto the objective lens and thus
the CCD. Both left and right images are captured on one CCD and
Focussing of the fibres is performed by adjusting the
position of the fibre mount. This is achieved with
micrometre precision using an XY positioning stage. The
fibre bundles are pivoted around a point 315mm from their
distal tips (close to the first image plane) to allow free-hand
rotations in a manner similar to a laparoscope passing
through a trocar port.
For validation, a removable rigid body with four optical
tracking markers was attached 117mm from the distal tip of
the fibroscope. This aspect of the system will be further
discussed in Section III.
The following calibration steps were then required to
allow for data acquisition:
• The orientation of the camera co-ordinate system in the
left camera image was defined manually to account for
the arbitrary rotation about the camera co-ordinate
system’s z-axis which occurs due to the rotation of the
fibre bundle between its two clamping points
• To account for this same arbitrary rotation about the z-
axis in the right image, its camera co-ordinate system
was calibrated to co-align with that of the left image
• Stereo camera calibration was performed to calculate the
intrinsic and extrinsic parameters and to correct for non-
linear radial lens distortion . This step was performed
manually due to the low resolution causing failure of the
automatic corner detection
• A hand-eye calibration to compute the relative rotation
and translation from the rigid body to the left camera
centre was then performed using the technique proposed
by Tsai and Lenz .
Example stereo images taken with the system on both an ex-
vivo porcine tissue sample and a silicone soft tissue phantom
are shown in Fig. 4. The completed system, showing the
fibroscope, rigid body and the optical system, is depicted in
Figure 4: Sample images captured with the stereo fibroscope of ex-vivo
porcine tissue (left) and a silicone soft tissue phantom (right).
Figure 5: Image showing the complete system. The optical setup including
fibre mount, objective lens and camera is shown on the lower left. The rigid
body used for validation purposes is shown in the top right.
B. SLAM Algorithm Design
A SLAM approach was adopted using an Extended
Kalman Filter system and stereo images giving 6DOF
SLAM similar to . A “constant velocity, constant
angular velocity” motion model is used with a deterministic
and a stochastic element to model unknown user motion.
1) Map management
The type of tissue or organ, distance between camera and
tissue and the illumination all affect the visual appearance of
the tissue. This problem is exacerbated by the limited
resolution of fibroscopes. To cope with this challenging
environment and improve runtime performance a sparse
feature map is used tracking up to 20 features at a time.
Features are detected using a Difference of Gaussian
detector, matched in the right and left image by searching
along the epipolar line and using a normalized cross
correlation. Outliers were removed using RANSAC. The
features were triangulated to estimate their 3D position
relative to the camera. This position was then reprojected
into the image plane and features with a large reprojection
error were rejected. In initial experiments we found that due
to the visual appearance of tissue the features clustered
around one or two regions in the image leading to poor
quality maps making accurate localization difficult. It has
been shown  that using a fish eye lens to increase the
field of view can improve SLAM, however here we are
limited to a small field-of-view and short working distance,
making feature selection and map management more
important. Ideally we want to observe the same features for
as long as possible in order to reduce uncertainty in the
features 3D position. We found the best approach to this
problem was to use features close to the edge of the image.
Although this makes map building and localisation more
robust, changes in illumination alter the appearance of
features making tracking more challenging. Specular
highlights can cause significant problems during tracking in
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a MIS environment. Specular highlights in the images were
detected using a manually defined threshold in the HSV
2) 3D Surface reconstruction
The solid surface representation is generated by
performing Delaunay triangulation on the SLAM map. This
meshing approach provides an estimate for every 3D point
within the observed and mapped environment. The mesh is
textured with images taken from the left fibroscope to build
up a realistic representation of the environment. Image
rectification is performed before the textures are applied to
the mesh in order to remove distortion. To improve the
visual appearance of the 3D reconstruction we search for
images which cover the largest number of points in the map
in order to generate models which are more consistent.
3) Honeycomb artifact removal
The light directed down the two image guides of the
fibroscope is captured by a distal CCD camera. As a result,
the structure of the individual fibres is visible in the image as
a honey comb structure (see Fig. 6), which can adversely
effect feature detection and tracking. Several different
approaches have been proposed for removal of the
honeycomb effect or defocusing the proximal imaging
optics, including estimations based on Bayer CCD patterns
and shaped Fourier filters  aimed at estimating the honey
Figure 6: a) Original test image captured by fibre bundle b) Test image after
honeycomb removal c) Original test image d) Fourier of original image e)
band pass filter applied in Fourier domain f - top) close up of (b); f- bottom)
close up of (a)
During the experiments, we found that sub-millimetre
movements of the optics relative to the CCD could lead to
changes in the image and movement of the honeycomb
structure on the CCD chip. As we could not rely on the
honeycomb structure being static relative to the camera we
needed a reliable and robust approach which did not need re-
calibrating each time the system is used. We found that
sufficient image restoration for tracking could be achieved
using a band pass filter  in the Fourier frequency space
followed by Gaussian smoothing. This successfully removed
the structure in the image without affecting the performance
of the tracker.
4) Feature tracking
Tracking tissue features is challenging as they may be
sparse, varying with different lighting conditions and
affected by specular highlights. Furthermore, the images
acquired by the proposed system are low in resolution due to
the limited number of fibres used. The intensity of the light
transmitted by the fibre bundles can vary leading to changes
in the visual appearance of features. To cope with this
environment a feature tracking system similar to  was
used in an active search context. This approach adapts to the
image content learning features online and directly from the
image space. The method is particularly suitable for MIS
images where features appear similar and may not be
globally distinctive. This approach learns the most
discriminative information for feature tracking, allowing it
to robustly track locally unique features. The approach has
been extended in this paper to include synthetically
generated data. Synthetic data is generated by warping the
image patch around the detected feature with an affine
transformation in order to make the feature tracking more
robust and able to track reliably from a single learning
frame. This is important for fiberscopic images because the
field of view is small and features may only appear for a
short period of time.
III. VALIDATION SETUP
A. Camera Motion Ground Truth Acquisition
In order to validate the accuracy of the camera motion as
reconstructed by the SLAM algorithm, a rigid body was
attached approximately 117mm from the distal tip of the
stereo fibroscope using four optical tracking markers
(Northern Digital Inc, Ontario, Canada). A Rigid Body co-
ordinate system () was defined at the origin of the
four markers. The position and orientation of this system
with respect to the World co-ordinate system () is
known at all instances in time. The measured rotations and
translations of this rigid body w.r.t. the world co-ordinate
system were transformed to the camera co-ordinate system
using the following transformation:
is provided by the optical tracker.
is obtained using a Hand-Eye transformation from the
origin of to the camera centre of the left fibre
. This was performed using techniques
similar to .
B. 3D Model Validation
To validate the SLAM algorithm, a silicone soft tissue
phantom was constructed and latex paints were used to
simulate specular reflections. A Computed Tomography
(CT) scan of the phantom was performed in order provide
ground truth. Prior to scanning, the model was embedded
with CT visible markers which were easily identifiable in the
resulting scan. During the data acquisition phase of the
experiment the location of each of the markers was
identified using a stylus which contained a second rigid body
of four optical tracking markers with a co-ordinate system
. This allowed for each of the markers to be identified
with respect to the world co-ordinate system and thus the
camera co-ordinate system.
A comparison between the surface of the CT
reconstruction and the point map generated by the SLAM
algorithm was performed. This required a process to find
points on the surface of the CT which corresponded to the
3D features in the SLAM map. Features detected in the
image were projected into the registered CT model from the
camera’s position given by the Optotrak. The projected ray
was traced through the 3D CT model to detect the first plane
it intersects. This point is taken to be the corresponding point
in the CT surface.
A. Camera Motion
Fig. 7 shows four reconstructed surfaces from the video
sequence. The blue line represents the ground truth camera
trajectory and the green line represents the trajectory
reconstructed by the SLAM algorithm. The stereo fibroscope
was moved by hand to explore unknown regions of the
phantom and to close a loop. It can be seen that the loop was
successfully closed. As the fibroscope moves into unknown
regions towards the end of the trajectory, an error
propagation leads to a small amount of drift being
introduced to the position estimate. This in part can be
attributed to the low resolution of the camera limiting the 3D
reconstruction and tracking accuracy.
Figure 7: Ground truth camera trajectory (blue) and SLAM reconstructed
camera trajectory (green) at four different frame intervals
Fig. 8 illustrates the trajectories when decomposed into
motions along the x, y and z axes for 1400 frames. The
absolute error in the three different axes was 1.94mm,
0.7mm and 1.7mm respectively. There was no rotation
around the z axis and only a minimal amount of rotation
around the x and y axes.
An additional ex-vivo experiment was carried out using
excised porcine tissue. The camera was moved in a motion
in which to close a loop and continue to explore in a similar
manner to the phantom experiment. As shown in Fig. 9, the
loop was successfully closed by the SLAM algorithm.
Figure 8: Trajectories decomposed into individual X, Y and Z components.
The ground truth from optical tracking markers is shown in blue and the
motion as reconstructed by the SLAM algorithm is shown in green
Figure 9: Reconstructed 3D surface and camera motion as generated by the
SLAM on an ex-vivo porcine tissue sample.
B. Surface Reconstruction
Fig. 10 illustrates the 3D surface generated by the SLAM
algorithm (right) alongside the ground truth 3D surface
extracted from the CT scan of the phantom (left) from three
different views. It can be seen that the scale, orientation and
geometry of the surfaces are visually similar. Local
differences in geometry can be attributed to the sparseness of
the SLAM map. Due to the meshing of the sparse map,
which fits planes between points, the recovered surface is
likely to be less accurate at representing local changes in
geometry. The overall reconstruction errors for the surface
for x,y and z are 2mm, 1.3mm and 2.9mm respectively. The
surface was approximately 35mm from the camera position
during the data capture. The reconstruction error is larger in
the z axis as expected since the resolution of the images and
small baseline between the fibre image guides makes stereo
triangulation less accurate.
f = 50 f = 300 f = 700 f = 1400
f = 50 f = 150 f = 500 f = 900
Figure 10: Reconstructed 3D surface as generated by the SLAM (right) and
co-registered CT ground truth data (left)
The paper demonstrates the feasibility of integrating twin
flexible fibre image guides in a stereo configuration to
capture images in an MIS environment. The challenges
overcome to construct and calibrate this bespoke image
system, along with image enhancement and robust feature
tracking techniques are presented. The resulting images were
successfully employed by a SLAM algorithm to both track
camera pose and motion and generate a 3D model of the
environment. It was anticipated that the limited resolution
offered by coherent fibre bundles might make this approach
infeasible. However, although the image resolution does
affect the final results, they clearly demonstrate that such an
approach is possible.
One of the limitations of a feature based optical approach
is that it can be affected by the paucity of tissue surface
features. One potential solution is to use structured light. The
sparse map limits the 3D model reconstruction accuracy.
This information could be improved by including dense
reconstruction information or combining it with other
approaches such as shape from shading. The next major
challenge to address for this system is that of tissue
deformation. Deformation occurs due to tool-interaction,
respiration and cardiac induced tissue motion. This can
violate the static world assumption made by SLAM.
Although the current system can cope with a very small
amount of deformation, as this increases the 3D map will be
inaccurate because it does not represent the deformation and
the fibroscope position estimate will be less accurate. One
potential application of the proposed framework is within a
catheter which utitilizes the stereo vision for targeting and
depth information for accurate focused energy delivery.
The authors would like to thank the Hamlyn Centre for
Robotic Surgery for funding this proof-of-concept study and
Drs Andrew Davison, Danail Stoyanov and Phillip Edwards
for their support and advice.
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