The DIET (Digital Image Elasto Tomography) system is a novel approach to screen for breast cancer using only
optical imaging information of the surface of a vibrating breast. 3D tracking of skin surface motion without the
requirement of external markers is desirable. A novel approach to establish point correspondences using pure skin
images is presented here. Instead of the intensity, motion is used as the primary feature, which can be extracted
using optical flow algorithms. Taking sequences of multiple frames into account, this motion information alone is
accurate and unambiguous enough to allow for a 3D reconstruction of the breast surface. Two approaches, direct
and probabilistic, for this correspondence estimation are presented here, suitable for different levels of calibration
information accuracy. Reconstructions show that the results obtained using these methods are comparable in
accuracy to marker-based methods while considerably increasing resolution. The presented method has high
potential in optical tissue deformation and motion sensing.
Colon cancer is the third most common type of cancer in the United States of America. Every year about 140,000
people are newly diagnosed with colon cancer. Early detection is crucial for a successful therapy. The standard
screening procedure is called colonoscopy. Using this endoscopic examination physicians can find colon polyps
and remove them if necessary. Adenomatous colon polyps are deemed a preliminary stage of colon cancer. The
removal of a polyp, though, can lead to complications like severe bleedings or colon perforation. Thus, only
polyps diagnosed as adenomatous should be removed. To decide whether a polyp is adenomatous the polyp's
surface structure including vascular patterns has to be inspected. Narrow-Band imaging (NBI) is a new tool to
improve visibility of vascular patterns of the polyps. The first step for an automatic polyp classification system
is the localization of the polyp. We investigate active contours for the localization of colon polyps in NBI image
data. The shape of polyps, though roughly approximated by an elliptic form, is highly variable. Active contours
offer the flexibility to adapt to polyp variation well. To avoid clustering of contour polygon points we propose
the application of active rays. The quality of the results was evaluated based on manually segmented polyps as
ground truth data. The results were compared to a template matching approach and to the Generalized Hough
Transform. Active contours are superior to the Hough transform and perform equally well as the template
matching approach.
While several mosaicking algorithms have been developed to compose endoscopic images of the internal urinary
bladder wall into panoramic images, the quantitative evaluation of these output images in terms of
geometrical distortions have often not been discussed. However, the visualization of the distortion level is
highly desired for an objective image-based medical diagnosis. Thus, we present in this paper a method to
create quality maps from the characteristics of transformation parameters, which were applied to the endoscopic
images during the registration process of the mosaicking algorithm. For a global first view impression,
the quality maps are laid over the panoramic image and highlight image regions in pseudo-colors according
to their local distortions. This illustration supports then surgeons to identify geometrically distorted structures
easily in the panoramic image, which allow more objective medical interpretations of tumor tissue in
shape and size. Aside from introducing quality maps in 2-D, we also discuss a visualization method to map
panoramic images onto a 3-D spherical bladder model. Reference points are manually selected by the surgeon
in the panoramic image and the 3-D model. Then the panoramic image is mapped by the Hammer-Aitoff
equal-area projection onto the 3-D surface using texture mapping. Finally the textured bladder model can
be freely moved in a virtual environment for inspection. Using a two-hemisphere bladder representation,
references between panoramic image regions and their corresponding space coordinates within the bladder
model are reconstructed. This additional spatial 3-D information thus assists the surgeon in navigation,
documentation, as well as surgical planning.
The treatment of urinary bladder cancer is usually carried out using fluorescence endoscopy. A narrow-band
bluish illumination activates a tumor marker resulting in a red fluorescence. Because of low illumination
power the distance between endoscope and bladder wall is kept low during the whole bladder scan, which is
carried out before treatment. Thus, only a small field of view (FOV) of the operation field is provided, which
impedes navigation and relocating of multi-focal tumors. Although off-line calculated panorama images can
assist surgery planning, the immediate display of successively growing overview images composed from single
video frames in real-time during the bladder scan, is well suited to ease navigation and reduce the risk of
missing tumors. Therefore we developed an image mosaicking algorithm for fluorescence endoscopy. Due to
fast computation requirements a flexible multi-threaded software architecture based on our RealTimeFrame
platform is developed. Different algorithm tasks, like image feature extraction, matching and stitching are
separated and applied by independent processing threads. Thus, different implementation of single tasks can
be easily evaluated. In an optimization step we evaluate the trade-off between feature repeatability and total
processing time, consider the thread synchronization, and achieve a constant workload of each thread. Thus,
a fast computation of panoramic images is performed on a standard hardware platform, preserving full input
image resolution (780x576) at the same time. Displayed on a second clinical monitor, the extended FOV of
the image composition promises high potential for surgery assistance.
The evolution of colon cancer starts with colon polyps. There are two different types of colon polyps, namely
hyperplasias and adenomas. Hyperplasias are benign polyps which are known not to evolve into cancer and,
therefore, do not need to be removed. By contrast, adenomas have a strong tendency to become malignant.
Therefore, they have to be removed immediately via polypectomy. For this reason, a method to differentiate
reliably adenomas from hyperplasias during a preventive medical endoscopy of the colon (colonoscopy) is highly
desirable. A recent study has shown that it is possible to distinguish both types of polyps visually by means
of their vascularization. Adenomas exhibit a large amount of blood vessel capillaries on their surface whereas
hyperplasias show only few of them. In this paper, we show the feasibility of computer-based classification of
colon polyps using vascularization features. The proposed classification algorithm consists of several steps: For
the critical part of vessel segmentation, we implemented and compared two segmentation algorithms. After a
skeletonization of the detected blood vessel candidates, we used the results as seed points for the Fast Marching
algorithm which is used to segment the whole vessel lumen. Subsequently, features are computed from this
segmentation which are then used to classify the polyps. In leave-one-out tests on our polyp database (56
polyps), we achieve a correct classification rate of approximately 90%.
Colorectal cancer is the third leading cause of cancer deaths in the United States of America for both women
and men. By means of early detection, the five year survival rate can be up to 90%. Polyps can to be grouped
into three different classes: hyperplastic, adenomatous, and carcinomatous polyps. Hyperplastic polyps are
benign and are not likely to develop into cancer. Adenomas, on the other hand, are known to grow into cancer
(adenoma-carcinoma sequence). Carcinomas are fully developed cancers and can be easily distinguished from
adenomas and hyperplastic polyps. A recent narrow band imaging (NBI) study by Tischendorf et al. has shown
that hyperplastic polyps and adenomas can be discriminated by their blood vessel structure. We designed a
computer-aided system for the differentiation between hyperplastic and adenomatous polyps. Our development
aim is to provide the medical practitioner with an additional objective interpretation of the available image data
as well as a confidence measure for the classification. We propose classification features calculated on the basis
of the extracted blood vessel structure. We use the combined length of the detected blood vessels, the average
perimeter of the vessels and their average gray level value. We achieve a successful classification rate of more than
90% on 102 polyps from our polyp data base. The classification results based on these features are compared to
the results of Local Binary Patterns (LBP). The results indicate that the implemented features are superior to
LBP.
A colon resection, necessary in case of colon cancer, can be performed minimally invasively by laparoscopy.
Before the affected part of the colon can be removed, however, the colon must be mobilized. A good technique
for mobilizing the colon is to use Gerota's fascia as a guiding structure, i. e. to dissect along this fascia, without
harming it. The challenge of this technique is that Gerota's fascia is usually difficult to distinguish from other
tissue.
In this paper, we present an approach to enhance the visual contrast between fatty tissue covered by Gerota's
fascia and uncovered fatty tissue, and the contrast of both structures to the remaining soft tissue in real time
(50 fields per second). As fasciae are whitish transparent tissues, they cannot be identified by means of their color
itself. Instead, we found that their most prominent feature to distinguish is the color saturation. To enhance
their visible contrast, we applied a non-linear transformation to the saturation.
An off-line evaluation was carried out consulting two specialists in laparoscopic colon resection. We presented
them four scenes from two different interventions in which our enhancement was applied together with the
original scenes. These scenes did not only contain situations where Gerota's fascia had to be found, but also
situations where aerosol from ultrasonically activated scissors inhibited the clear vision, or situations where
critical structures such as the ureter or nerves had to be identified under fascial tissue. The surgeons stated that
our algorithm clearly offered an information gain in all of the presented scenes, and that it did not impair the
clear vision in case of aerosol or the visibility of critical structures. So the colon mobilization could be carried
out easier, faster, and safer.
In the subsequent clinical on-line evaluation, the specialists confirmed the positive effect of the proposed algorithm
on the visibility of Gerota's fascia.
A number of image analysis tasks of the heart region have to cope
with both the problem of respiration and heart contraction. While
the heart contraction status can be estimated based on the ECG,
respiration status estimation must be based on the images themselves, unless additional devices for respiration measurements
are used. Since diaphragm motion is closely linked to respiration,
we describe a method to detect and track the diaphragm in x-ray
projections. We model the diaphragm boundary as being approximately
circular. Diaphragm detection is then based on edge detection
followed by a Hough transform for circles. To avoid that the
detection algorithm is misled by high frequency image content, we
first apply a morphological multi-scale top hat operator. A Canny
edge detector is then applied to the top hat filtered images. In the
edge images, the circle corresponding to the diaphragm boundary is
found by the Hough transform. To restrict the search in the 3D Hough
parameter space (parameters are circle center coordinates and
radius), prior anatomical knowledge about position and size of the
diaphragm for the given image acquisition geometry is taken into
account. In subsequent frames, diaphragm position and size are
predicted from previous detection and tracking results. For each
detection result, a confidence measure is computed by analyzing the
Hough parameter space with respect to the goodness of the peak
giving the circle parameters and by analyzing the coefficient of
variation of the pixel that form the circle described by the maximum
in Hough parameter space. If the confidence is not sufficiently high
-- indicating a poor fit between the Hough circle and true diaphragm
boundary -- the detection result is optionally refined by an active
contour algorithm.
The fusion of information in medical imaging relies on accurate registration of the image content coming often from different sources. One of the strongest influences on the movement of organs is the patient’s respiration. It is known, that respiration status can be measured by comparing the projection images of the chest. Since the diaphragm compresses the soft tissue above, the level of similarity to a reference projection image in extremely inhaled or exhaled status gives an indication of the patient’s respiration status. If the images to be registered are generated under different conditions the similarity with a common reference image is calculated on different scales and therefore cannot be compared directly. The proposed solution uses two reference images acquired in extremely inhaled and exhaled position. By comparing the images with two references and by combining the similarity results, changes in respiration depth between acquisitions can be detected. With normal breathing, the similarity to one of the reference images increases while the similarity to the other one decreases over time or vice versa. If the patient’s respiration exceeds the respiration span of the reference images, the similarity to both reference images decreases. By using not only the similarity values but also their derivatives over time, changes in respiration depth therefore can be detected and the image fusion algorithm can act accordingly e.g. by removing images that exceed the valid respiration span.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.