Biomedical subjects

T Kanade

Publications and source records attributed to T Kanade.

8 recordsLinked to original sources

PubMed2000

An image overlay system for medical data visualization.

Image Overlay is a computer display technique which superimposes computer images over the user's direct view of the real world. The images are transformed in real-time so they appear to the user to be an integral part of the surrounding environment. By using Image Overlay with three-dimensional medical images such as CT reconstructions, a surgeon can visualize the data 'in-vivo', exactly positioned within the patient's anatomy, and potentially enhance the surgeon's ability to perform a complex procedure. This paper describes prototype Image Overlay systems and initial experimental results from those systems.

Computer Graphics↗

PubMed2000

Computational model of DIC microscopy for reconstructing 3-D specimens.

Differential Interference Contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent non-linear relation between the object properties and the image intensity makes quantitative analysis difficult. As a first step towards measuring optical properties of objects from DIC images, we develop a model for the image formation process using methods consistent with energy conservation laws. We verify our model by comparing real image data of manufactured specimens to simulated images of virtual objects. As the next step, we plan to use this model to reconstruct the three-dimensional properties of unknown specimens.

Computer Simulation↗

PubMed1999

Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding.

The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report, we compare the results with this automated system with those of manual FACS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from videotape by certified FACS coders. Fifteen action units and action unit combinations that occurred a minimum of 25 times were selected for automated analysis. Facial features were automatically tracked in digitized image sequences using a hierarchical algorithm for estimating optical flow. The measurements were normalized for variation in position, orientation, and scale. The image sequences were randomly divided into a training set and a cross-validation set, and discriminant function analyses were conducted on the feature point measurements. In the training set, average agreement with manual FACS coding was 92% or higher for action units in the brow, eye, and mouth regions. In the cross-validation set, average agreement was 91%, 88%, and 81% for action units in the brow, eye, and mouth regions, respectively. Automated face analysis by feature point tracking demonstrated high concurrent validity with manual FACS coding.

Adolescent↗

PubMed1998

Volumetric object modeling for surgical simulation.

Surgical simulation has many applications in medical education, surgical training, surgical planning and intra-operative assistance. However, extending current surface-based computer graphics methods to model phenomena such as the deformation, cutting, tearing or repairing of soft tissues poses significant challenges for real-time interactions. This paper discusses the use of volumetric methods for modeling complex anatomy and tissue interactions. New techniques are introduced that use volumetric methods for modeling soft-tissue deformation and tissue cutting at interactive rates. An initial prototype for simulating arthroscopic knee surgery is described which uses volumetric models of the knee derived from 3-D magnetic resonance imaging, visual feedback via real-time volume and polygon rendering, and haptic feedback provided by a force-feedback device.

Arthroscopy↗

PubMed1998

The Otto Aufranc Award. Image guided navigation system to measure intraoperatively acetabular implant alignment.

There has been little clinical research to examine the effects of patient positioning and pelvic motion on the alignment of the acetabular implant during total hip replacement surgery. Until now, no tools were capable of accurately measuring these variables during the actual procedure. As part of a broader program in medical robotics and computer assisted surgery, a clinical system has been developed that includes several enabling technologies. The hip navigation system (HipNav) continuously and precisely measures pelvic location and tracks relative implant alignment intraoperatively. HipNav technology is used to gauge current clinical practice and provide intraoperative feedback to surgeons with the goal of improving the precision and accuracy of acetabular alignment during total hip replacement. This system provides surgeons with a new class of image guided measurement tools and assist devices. These tools successfully were introduced into the clinical practice of surgery with results showing the following: (1) There exist unpredictable and large variations in the initial position of patients' pelves on the operating room table and significant pelvic movement during surgery and during intraoperative range of motion testing; (2) current mechanical acetabular alignment guides do not account for these variations, and result in variable and in the majority of cases unacceptable acetabular alignment; and (3) press fitting oversized acetabular components influences the final cup orientation.

Anthropometry↗

PubMed1995

Techniques for fast and accurate intrasurgical registration.

The goal of intrasurgical registration is to establish a common reference frame between presurgical and intrasurgical three-dimensional data sets that correspond to the same anatomy. This paper presents two novel techniques that have application to this problem, high-speed pose tracking and intrasurgical data selection. In the first part of this paper, we describe an approach for tracking the pose of arbitrarily shaped rigid objects at rates up to 10 Hz. Static accuracies on the order of 1 mm in translation and 1 degree in rotation have been achieved. We have demonstrated the technique on a human face using a high-speed VLSI range sensor; however, the technique is independent of the sensor used or the anatomy tracked. In the second part of this paper, we describe a general purpose approach for selecting near-optimal intrasurgical registration data. Because of the high costs of acquisition of intrasurgical data, our goal is to minimize the amount of data acquired while ensuring registration accuracy. We synthesize near-optimal intrasurgical data sets, based on an analysis of differential surface properties of presurgical data. We demonstrate, using data from a human femur, that discrete-point data sets selected using our method are superior to those selected by human experts in terms of the resulting pose-refinement accuracy.

Algorithms↗

PubMed1995

Vision-based object registration for real-time image overlay.

This paper presents a computer vision-based technique for object registration, real-time tracking, and image overlay. The capability can be used to superimpose registered medical images such as those from CT or MRI on to a video image of a patient's body. Real-time object registration enables an image to be overlaid consistently on to objects even while the objects and cameras viewing it are moving. Object registration is composed of feature tracking, feature correspondence, and pose calculation of objects. This technique is based on geometric models of objects, but it can be extended so that some image overlay is possible without a prior model of the object.

Artificial Intelligence↗

PubMed1993

Shape and motion from image streams: a factorization method.

Inferring scene geometry and camera motion from a stream of images is possible in principle, but it is an ill-conditioned problem when the objects are distant with respect to their size. We have developed a factorization method that can overcome this difficulty by recovering shape and motion without computing depth as an intermediate step. An image stream can be represented by the 2F x P measurement matrix of the image coordinates of P points tracked through F frames. Under orthographic projection this matrix is of rank 3. Using this observation, the factorization method uses the singular value decomposition technique to factor the measurement matrix into two matrices, which represent object shape and camera motion, respectively. The method can also handle and obtain a full solution from a partially filled-in measurement matrix, which occurs when features appear and disappear in the image sequence due to occlusions or tracking failures. The method gives accurate results and does not introduce smoothing in either shape or motion. We demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions.

Journal Article↗