Registration and three-dimensional reconstruction of autoradiographic images by the disparity analysis method.

Quantitative autoradiography is a powerful radioisotopic-imaging method for neuroscientists to study local cerebral blood flow and glucose-metabolic rate at rest, in response to physiologic activation of the visual, auditory, somatosensory, and motor systems, and in pathologic conditions. Most autoradiographic studies analyze glucose utilization and blood flow in two-dimensional (2D) coronal sections. With modern digital computer and image-processing techniques, a large number of closely spaced coronal sections can be stacked appropriately to form a three-dimensional (3D) image. 3D autoradiography allows investigators to observe cerebral sections and surfaces from any viewing angle. A fundamental problem in 3D reconstruction is the alignment (registration) of the coronal sections. A new alignment method based on disparity analysis is presented, which can overcome many of the difficulties encountered by previous methods. The disparity analysis method can deal with asymmetric, damaged, or tilted coronal sections under the same general framework, and it can be used to match coronal sections of different sizes and shapes. Experimental results on alignment and 3D reconstruction are presented.

A mathematical model for computer image tracking.

A mathematical model using an operator formulation for a moving object in a sequence of images is presented. Time-varying translation and rotation operators are derived to describe the motion. A variational estimation algorithm is developed to track the dynamic parameters of the operators. The occlusion problem is alleviated by using a predictive Kalman filter to keep the tracking on course during severe occlusion. The tracking algorithm (variational estimation in conjunction with Kalman filter) is implemented to track moving objects with occasional occlusion in computer-simulated binary images.

Statistical pattern classification with binary variables.

Binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.

Boundary localization in texture segmentation.

Localizing boundaries between textured image regions without sacrificing the labeling accuracy of interior regions remains a problem in segmentation. Difficulties arise because of the conflicting requirements of localization and labeling. Boundary localization usually demands observing the features over small neighborhoods, whereas labeling accuracy increases with the size of the observation neighborhood. This problem is further exacerbated in texture segmentation by the spatially distributed nature of texture features. In this correspondence, we develop a multiresolution approach that combines localized and distributed features to directly address boundary localization in texture segmentation. Maximum localization is achieved by using the gray-level discontinuities at the boundary between textures to define the boundary. The properties that characterize the gray-level discontinuity at texture boundaries are developed and an algorithm is designed to localize the boundary using these discontinuities. This segmentation algorithm is implemented and successfully tested on a set of Brodatz texture mosaics and AVHRR satellite imagery.

Enhancement and compression of digital chest radiographs.

The application of digital technologies to chest radiography holds the promise of routine application of image processing techniques to effect image enhancement. Because of their inherent spatial resolution, however, digital chest images impose severe constraints on data storage devices. Compression of these images will relax such constraints and facilitate image transmission on a digital network. We evaluated an algorithm for enhancing digital chest images that has allowed significant data compression while improving the diagnostic quality of the image. This algorithm is based on the photographic technique of unsharp masking. Image quality was measured with respect to the task of tumor detection and compression ratios as high as 2:1 were achieved. This compression can be supplemented by irreversible methods.