Manifold image processing for see through effect in

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 392
MANIFOLD IMAGE PROCESSING FOR SEE-THROUGH EFFECT IN
LAPAROSCOPIC SURGERIES
Sindhiya Devi.R1
, Saravanaselvi.P2
, Steena.J3
1, 3
II year M.E. student, 2
Assistant Professor, Department of ECE, Einstein College of Engineering, Tirunelveli,
Tamilnadu, India
Abstract
This approach shows the processing of multiple images of a virtually transparent epidermal imagery (VTEI) system for LESS surgical
procedure. In this paper, a novel Homographic Image Mosaicking and Morphing (HIMM) procedure is proposed for image-stitching
and also for compensating the irregular surfaces, where scale invariant feature detection (SIFT) algorithm for detecting a set of
feature points, vector field consensus (VFC) algorithm for mismatch rejection, Gauss Newton algorithm for non – linear minimization
and backward image warping algorithms are used to obtain the see-through effect. This view is then noticeable to the surgeon in VTEI
for laparoscopic surgeries.
Keywords: Biomedical devices, VTEI system, feature detection, mismatch rejection, image stitching
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1. INTRODUCTION
Minimally Invasive Surgery (MIS), also called Laparoscopic
surgery or keyhole surgery is a technique for surgeries in
which the procedures are performed through tiny incisions in
the abdomen (usually 0.5–1.5 cm) rather than the larger
incisions in open cavity surgeries. There are a variety of
advantages of laparoscopic surgery over the open cavity
surgical procedures. These include: Reduced hemorrhaging,
smaller incision, reduced pain and shortened recovery time.
However, this procedure is more difficult for the surgeon
when compared to the traditional open surgery with limited
range of motion, poor depth perception and difficult hand-eye
correlation. The Minimally Invasive Surgical procedure also
retain some challenges to surgeons which include restricted
view and limited number of lookouts fixed by the insertion
spots, an overhead screen that reveals the video from the
videoscope but does not have a regular and rich indication of
orientation of the video.
Minimally invasive surgery has become a standard of care for
the medication of many benign and malignant gynaecological
conditions. Laparoendoscopic single-site surgery (LESS), also
known as single-port surgery, is a fresh and innovative,
promptly onrushing minimally invasive technique. LESS
enhances the aesthetic value of minimally invasive surgery by
providing a single incision, where a trocar port can be inserted
in the umbilicus. This appears to be a scar-free treatment.
This paper projects on the enhancement of LESS surgery with
a Virtually Transparent Imagery (VTEI) system. It focuses on
processing manifold imagery of the VTEI system.
After receiving the HD video signal from various sensor
sources inside the body, image processing must be cautiously
accomplished to provide a see-through effect for the surgeons.
Various approaches [2]–[5] for visualization have been
proposed to achieve “seeing through” effect. Image
mosaicking is a well-studied research topic in computer vision
[6] and has been widely applied in medical image analysis
[7]–[10]. We present a novel Homographic Image Mosaicking
and Morphing (HIMM) algorithm to stitch the images from
various sensors to achieve a transparent system. Additionally,
the proposed HIMM algorithm will alleviate the surgical tool
overlay problem that is present in other approaches.
This paper is organized into four sections. Methodology is
described in section II, the experimentation and the results are
discussed in section III. Conclusion of this work is described
in section IV.
2. METHODOLOGY
There are many rich attributes to be investigated before VTEI
can become a reality in clinical assessments. Many surgeons
have grown their comfortability towards HD-quality wired
endoscopes.
The problem of using multiple cameras for image mosaicking
is aggravated by the shape of the insufflated abdomen. Thus,
we need a new hybrid stitching algorithm to replicate the
perfect display. Hence we present a homographic image
mosaicking and morphing algorithm to “focus” correctly on
the concave surface of the abdomen. The wirelessly
transmitted videos from two cameras from different viewing
points are stitched together with partial overlapping areas to
create a perfect panoramic video with high resolution.

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We designed a new image mosaicking method particularly for
our VTEI system, which is when compared with conventional
image mosaicking technique has some modifications such that
we use the recently proposed vector field concensus (VFC)
[11] instead of scale invariant feature transform (SIFT) [12] to
reject mismatches.
To begin the HIMM procedure, we assume that there is a set
of images transmitted to the computer from the cameras at any
given time. These images will be mosaicked together. The
procedure of HIMM is found in [6] can be summarized as
follows: First, a set of SIFT feature points are detected in two
adjacent images using the method which is presented in [12].
The points in the first image are denoted as xi , yi and the
corresponding points in the second image as xi’, yi’ . The
points between the two images satisfy the homography
relationship between the images.To estimate the homographic
matrix the error, E, must be minimized, which can be written
as
min E min x x ′ y y ′
Since the relationship between (xi’’,yi’’) and (xi,yi) is non-
linear, Gauss-Newton optimization is used to estimate the
homography parameters. Once when the homography matrices
are computed, we will be able to warp one image to another in
a piecewise manner. First, a reference frame is selected and
then all the other frames are warped back into the reference
image’s coordinate. Since forward mapping might create black
seams for those pixels that are not mapped, we use the
backward mapping incorporated with bilinear interpolation.
The overlap area of the final composite image might look
blurry. Hence we use simple feathering to solve this problem.
3. RESULTS AND DISCUSSIONS
In this paper, we present a novel HIMM algorithm for
Homographic Image Mosaicking and Morphing to create a
see-through effect of the surgical area for the surgeons.
Planar 2D images are projected directly onto the insufflated
abdomen to give surgeons an open-cavity view of the surgical
area. This projection over 2D images introduces a distortion
effect that is disorienting for the surgeon. To overcome this
effect, the mosaicking is combined with a morphing
algorithm. This combination of mosaicking and morphing into
a single algorithm is referred to as HIMM and it can be run in
real-time.
(a) (b)
(c)
Fig -1: HIMM algorithm output. a)Image captured by the left
camera, b) Image captured by the right camera, c) Image after
fusion.
The images shown in fig 1(a) and fig 1(b) are obtained from
two cameras in different views. The final blending result is
shown in Fig. 1(c) where the original left and right images are
perfectly mosaicked and morphed into a single display. Thus
the HIMM algorithm can maintain a correspondence between
the various inputs and also it can provide the open cavity view
needed by the surgeons.
4. CONCLUSIONS
Next-generation biomedical devices for advanced minimally
invasive surgeries will require wireless links to get data from
in vivo image sensors. Our work deals with the process of
image mosaicking and morphing by using a novel HIMM
algorithm to create an open cavity view to the surgeons.
REFERENCES
[1]. Y. Sun, A. Anderson, C. Castro, B. Lin, and R. Gitlin,
“Virtually transparent epidermal imagery for laparo-
endoscopic single-site surgery,” in Proc. Int. Conf.
IEEE Engineering in Medicine and Biology Soc.,
Aug.2011.
[2]. M. Blackwell, C. Nikou, A. Digioia, and T. Kanade,
“An image overlay system for medical data

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Keller, A. State, J. R. Crawford, P. Rademacher, S. H.
Drake, and A. A. Meyer, “Augmented reality
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[12]. D. G. Lowe, “Distinctive image features from scale-
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BIOGRAPHIES
Sindhiya Devi.R received the B.E degree in
2010, through Anna University, Chennai,
India and currently pursuing M.E in the same
university. Her research interests include
computer vision and medical image
processing.
Saravanaselvi.P received the B.E and M.E
degrees in 2001 and 2009 respectively
through Anna University, Chennai, India.
Currently, she is an Assistant Professor in
Einstein College of Engineering,
Tirunelveli, India. Her research interests
include computer vision.
Steena.J received the B.E degree in 2012
through Anna University, Chennai, India
and she is currently pursuing M.E in the
same university. Her research interests
include computer vision.

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