Image registration is an important enabling technology in medical image analysis. The current emphasis is on development and validation of application-specific non-rigid techniques, but there is already a plethora of techniques and terminology in use. In this paper we discuss the current state of the art of non-rigid registration to put on-going research in context and to highlight current and future clinical applications that might benefit from this technology. The philosophy and motivation underlying non-rigid registration is discussed and a guide to common terminology is presented. The core components of registration systems are described and outstanding issues of validity and validation are confronted.

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Non-rigid image registration: theory and practice

A review of 3D/2D registration methods for image-guided interventions

Visual-assisted guidance of an ultra-thin flexible endoscope to a predetermined region of interest within a lung during a bronchoscopy procedure. The region may be an opacity-identified by non-invasive imaging methods, such as high-resolution computed tomography (HRCT) or as a malignant lung mass that was diagnosed in a previous examination. An embedded position sensor on the flexible endoscope indicates the position of the distal tip of the probe in a Cartesian coordinate system during the procedure. A visual display is continually updated, showing the present position and orientation of the marker in a 3-D graphical airway model generated from image reconstruction. The visual display also includes windows depicting a virtual fly-through perspective and real-time video images acquired at the head of the endoscope, which can be stored as data, with an audio or textual account.

Catheterscope 3D Guidance and Interface System

An apparatus and method for providing image acquisition and/or image display in a limited region of interest (ROI). The apparatus comprises a micro electromechanical system (MEMS), preferably integrating a light source, a cantilever, a lens, an actuator, a light detector, and a position sensor. The light source provides light for illuminating the ROI, displaying an image, providing a therapy, and/or performing other functions. The cantilever comprises a resin waveguide with a fixed end attached to a substrate that supports many or all other components. A free end of the cantilever is released from the substrate during fabrication and includes the lens. The actuator scans the free end in orthogonal directions to illuminate the ROI or display an image. The position sensors detect the position of the free end for control. The light detector receives light backscattered from the ROI separate from, or at the fixed end the cantilever.

Integrated optical scanning image acquisiting and display

This paper presents a new method for image-guided surgery called image-enhanced endoscopy. Registered real and virtual endoscopic images (perspective volume renderings generated from the same view as the endoscope camera using a preoperative image) are displayed simultaneously; when combined with the ability to vary tissue transparency in the virtual images, this provides surgeons with the ability to see beyond visible surfaces and, thus, provides additional exposure during surgery. A mount with four photoreflective spheres is rigidly attached to the endoscope and its position and orientation is tracked using an optical position sensor. Generation of virtual images that are accurately registered to the real endoscopic images requires calibration of the tracked endoscope. The calibration process determines intrinsic parameters (that represent the projection of three-dimensional points onto the two-dimensional endoscope camera imaging plane) and extrinsic parameters (that represent the transformation from the coordinate system of the tracker mount attached to the endoscope to the coordinate system of the endoscope camera), and determines radial lens distortion. The calibration routine is fast, automatic, accurate and reliable, and is insensitive to rotational orientation of the endoscope. The routine automatically detects, localizes, and identifies dots in a video image snapshot of the calibration target grid and determines the calibration parameters from the sets of known physical coordinates and localized image coordinates of the target grid dots. Using nonlinear lens-distortion correction, which can be performed at real-time rates (30 frames per second), the mean projection error is less than 0.5 mm at distances up to 25 mm from the endoscope tip, and less than 1.0 mm up to 45 mm. Experimental measurements and point-based registration error theory show that the tracking error is about 0.5-0.7 mm at the tip of the endoscope and less than 0.9 mm for all points in the field of view of the endoscope camera at a distance of up to 65 mm from the tip. It is probable that much of the projection error is due to endoscope tracking error rather than calibration error. Two examples of clinical applications are presented to illustrate the usefulness of image-enhanced endoscopy. This method is a useful addition to conventional image-guidance systems, which generally show only the position of the tip (and sometimes the orientation) of a surgical instrument or probe on reformatted image slices.

Implementation, calibration and accuracy testing of an image-enhanced endoscopy system

Tracking of a bronchoscope using epipolar geometry analysis and intensity-based image registration of real and virtual endoscopic images.

This paper proposes an improved method for camera motion tracking of a real endoscope based on registration endoscopic images and Computerized Tomography (CT) images. Camera motion estimation is a fundamental function of an endoscope navigation system, which provides useful navigation information to medical doctors during endoscopic examinations. Our previous method consists of two steps: (1) rough estimation of camera motion using optical flow information and (2) precise estimation performed by the image based registration technique. The problem of the previous method was that only the forward and the backward motion of the camera were estimated by the optical flow information. To solve this problem, the proposed method effectively uses the change in texture information on real endoscopic video images and estimates the camera motion by mapping the texture to the organ's 3-D shape generated from the CT images. We roughly estimate the all motion of the camera using texture information of the organ's wall, then the precise estimation is performed by using image based registration method. We applied the proposed method to the real bronchoscopic video images and 3-D X-ray CT images. The results sowed that the proposed method was superior to our previous method in many cases.

Camera motion tracking of real endoscope by using virtual endoscopy system and texture information

This paper describes a method for camera motion estimation based on real endoscope images and CT images for an endoscope navigation system We have developed an endoscope navigation system that shows the navigation information during an endoscope examination In this system, we need to Out side view and estimate the real endoscope camera motion Our method tracks the camera motion by using an imagebased registration technique We applied the proposed method to the video images of the real bronchoscopy and 3-D X-ray CT images Experimental results showed that the method could track the camera motion corresponding to the real endoscope Figure 1: Endoscope navigation system correctly

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Jun Sugiyama

Papers

Tracking of a bronchoscope using epipolar geometry analysis and intensity-based image registration of real and virtual endoscopic images.

Camera motion tracking of real endoscope by using virtual endoscopy system and texture information

Camera Motion Estimation Based on Real Endoscope Images and CT Images for Endoscope Navigation System