Shoupu Chen

Incorporating spatial information into entropy estimates to improve multimodal image registration

We propose an approach to incorporate spatial information into the estimate of the entropy of an image by partitioning a high-dimensional space into regions over which a one-dimensional histogram can be indexed. This approach obviates the need for the construction of high-dimensional histograms and makes no assumptions on the form of the high-dimensional probability mass function. We use this partitioned estimate of entropy to compute image similarity measures, useful in image registration, and we examine the convergence properties of the registration process

Method of image fusion for radiotherapy

Patient setup error is one of the major causes of tumor position uncertainty in radiotherapy for extracranial targets, which can result in a decreased radiation dose to the tumor and an increased dose to the normal tissues. Therefore, it is a common practice to verify the patient setup accuracy by comparing portal images with a digitally reconstructed radiograph (DRR) reference image. This paper proposes a practical method of portal image and DRR fusion for patient setup verification. As a result of the mean intensity difference between the inside and outside of the actual radiation region in the portal image, the image fusion in this work is fulfilled by applying an image registration process to the contents inside or outside of the actual radiation region in the portal image and the relevant contents that are extracted, accordingly, from the DRR image. The image fusion can also be fulfilled statistically by applying two separate image registration processes to the inside and outside of the actual radiation regions. To segment the image registration contents, automatic or semiautomatic region delineation schemes are employed that aim at minimizing users operation burden, while at the same time maximizing the use of human intelligence. To achieve an accurate and fast delineation, this paper proposes using adaptive weight in the conventional level-set contour-finding algorithm for the automatic delineation scheme, as well as the use of adaptive banding in the conventional Intelligent Scissors algorithm for the semiautomatic delineation scheme.

Toward effectively generating environment maps

We explore a cost-effective approach to creating envi- ronment maps. Unlike other environment map generating systems that use specially designed mirrors or sensing devices, our system uses off-the-shelf digital cameras. This makes our system poten- tially acquirable by average consumers. In our design, the two digital cameras are displaced vertically with parallel optical axes, and the rotation axis passes through the focal points of the two cameras. This vertical displacement arrangement overcomes the problem of having inconsistent occlusions in two adjacent stereo image sets that happen in a horizontally displaced stereo camera system. To obtain dense spatial images, our system uses color-information- based algorithms to propagate disparity values from matched fea- ture points to unmatched feature points and featureless areas. Schemes of transforming local 3-D (X,Y,Z) data to a world coordi- nate system are also devised to smoothly stitch 3-D data from indi- vidual frames for virtual reality modeling language graphics display.

A practical approach to creating environment maps using digital images

Conventional panoramic stereo camera heads have a horizontal displacement structure. When a camera head is used in generating a spatial panorama, it may encounter occlusion inconsistency in the overlap region of two adjacent viewing angles; that is, occlusion may occur from one viewing angle, but not from an adjacent angle. Also, computing spatial panorama may become unnecessarily complex. This paper reports a solution to the above problems by using a dual camera head with the two cameras being displaced vertically with parallel optical axes, and by letting the rotation axis pass through the focal points of the two cameras.