2 research outputs found
An Elastic Image Registration Approach for Wireless Capsule Endoscope Localization
Wireless Capsule Endoscope (WCE) is an innovative imaging device that permits
physicians to examine all the areas of the Gastrointestinal (GI) tract. It is
especially important for the small intestine, where traditional invasive
endoscopies cannot reach. Although WCE represents an extremely important
advance in medical imaging, a major drawback that remains unsolved is the WCE
precise location in the human body during its operating time. This is mainly
due to the complex physiological environment and the inherent capsule effects
during its movement. When an abnormality is detected, in the WCE images,
medical doctors do not know precisely where this abnormality is located
relative to the intestine and therefore they can not proceed efficiently with
the appropriate therapy. The primary objective of the present paper is to give
a contribution to WCE localization, using image-based methods. The main focus
of this work is on the description of a multiscale elastic image registration
approach, its experimental application on WCE videos, and comparison with a
multiscale affine registration. The proposed approach includes registrations
that capture both rigid-like and non-rigid deformations, due respectively to
the rigid-like WCE movement and the elastic deformation of the small intestine
originated by the GI peristaltic movement. Under this approach a qualitative
information about the WCE speed can be obtained, as well as the WCE location
and orientation via projective geometry. The results of the experimental tests
with real WCE video frames show the good performance of the proposed approach,
when elastic deformations of the small intestine are involved in successive
frames, and its superiority with respect to a multiscale affine image
registration, which accounts for rigid-like deformations only and discards
elastic deformations
Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy
This paper attempts to provide the reader a place to begin studying the
application of computer vision and machine learning to gastrointestinal (GI)
endoscopy. They have been classified into 18 categories. It should be be noted
by the reader that this is a review from pre-deep learning era. A lot of deep
learning based applications have not been covered in this thesis