Using curve-fitting of curvilinear features for assessing registration of clinical neuropathology with in vivo MRI.

Traditional neuropathological examination provides information about neurological disease or injury of a patient at a high-resolution level. Correlating this type of post mortem diagnosis with in vivo image data of the same patient acquired by non-invasive tomographic scans greatly complements the interpretation of any disease or injury. We present the validation of a registration method for correlating macroscopic pathological images with MR images of the same patient. This also allows for 3-D mapping of the distribution of pathological changes throughout the brain. As the validation deals with datasets of widely differing sampling, we propose a method using smooth curvilinear anatomical features in the brain which allows interpolation between wide-spaced samples. Curvilinear features are common anatomically, and if selected carefully have the potential to allow determination of the accuracy of co-registration across large areas of a volume of interest.

Integrating watersheds and critical point analysis for object detection in discrete 2D images.

This paper presents an improved method for the detection of "significant" low-level objects in medical images. The method overcomes topological problems where multiple redundant saddle points are detected in digital images. Information derived from watershed regions is used to select and refine saddle points in the discrete domain and to construct the watersheds and watercourses (ridges and valleys). We also demonstrate an improved method of pruning the tessellation by which to define low level objects in zero order images. The algorithm was applied on a set of medical images with promising results. Evaluation was based on theoretical analysis and human observer experiments.