Abstract
The most common medical diagnostic method for urinary bladder cancer is cystoscopy. This inspection of the bladder is performed by a rigid endoscope, which is usually guided close to the bladder wall. This causes a very limited field of view; difficulty of navigation is aggravated by the usage of angled endoscopes. These factors cause difficulties in orientation and visual control. To overcome this problem, the paper presents a method for extracting 3D information from uncalibrated endoscopic image sequences and for reconstructing the scene content. The method uses the SURF-algorithm to extract features from the images and relates the images by advanced matching. To stabilize the matching, the epipolar geometry is extracted for each image pair using a modified RANSAC-algorithm. Afterwards these matched point pairs are used to generate point triplets over three images and to describe the trifocal geometry. The 3D scene points are determined by applying triangulation to the matched image points. Thus, these points are used to generate a projective 3D reconstruction of the scene, and provide the first step for further metric reconstructions.
Highlights
With about 68 810 new cases in 2008 in the United States [1], bladder cancer is a common disease of the urinary system
This paper presents a method for extracting 3D information from an uncalibrated endoscopic image sequence, which is used for a projective 3D bladder reconstruction
This paper presents a method for reconstructing 3D scene content from uncalibrated endoscopic sequences based on SURF-features
Summary
With about 68 810 new cases in 2008 in the United States [1], bladder cancer is a common disease of the urinary system. Tumors are usually inspected and treated by endoscopic interventions. The inspection is usually performed close to the bladder wall, which is why the field of view is very limited. This paper presents a method for extracting 3D information from an uncalibrated endoscopic image sequence, which is used for a projective 3D bladder reconstruction. In further steps, this information can be used for auto calibration of the camera, which leads to the desired metric reconstruction. The paper is organized as follows: In section 2 the image preprocessing, the mathematical reconstruction and the reconstruction algorithms are described.
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