Rigid motion steerability for multiscale stochastic models of 3D-textures applied to soft tissue segmentation/identification in 3D-biomedical images

University Of Houston, Houston TX

Investigators

Abstract

Modern medicine and biology have been enormously benefited from the advancement of imaging. New devices and acquisition methods enabled the first images of viruses. Resolution levels for diagnostic imaging are now at the order of a few hundred microns and in 3D; e.g. MRI or CT scans. Despite of all these advances some information in medical images is latent and extracting it is often a tedious task. Achieving finer resolution levels does not automatically make every tissue visible to the eye of the practitioner. The expansion of the imaging frontiers not only increases grossly the volume of the available data but also makes to want to extract more information from an image. Thus, there is an ever growing demand for the development of reliable, automated or semi-automated image analysis tools. With this goal in mind the interdisciplinary group of investigators in this project aims in making theoretical and algorithmic contributions that can lead to the development of such tools. The problem motivating this project is how to identify or segment soft-tissues that are of interest to medical practitioners or biologists with high spatial accuracy in 3D-images. To our detriment, most of the time tissues of diagnostic interest have great variability, small volume, low contrast and are corrupted by non-standard noise. Based on the premise that soft-tissues are associated with 3D-textures, the investigators approach soft-tissue discrimination/identification as segmentation/identification of the 3D-textures resulting from the tissues of interest. Notable efforts have been made to solve this problem in 2D but in 3D it is practically untouched. To achieve high spatial accuracy in the segmentation/identification of 3D-textures the investigators will build novel probabilistic models for 3D-rigid motion invariant texture signatures. This will reduce or may even eliminate classification errors due to the positioning of a tissue in the 3D-space. To extract such signatures we will characterize and thoroughly study multiscale data representations that are covariant (steerable) with respect to 3D-rigid motions. A major challenge of this project is to extract 3D-rigid motion invariant texture signatures with reasonable length and adopt probabilistic models governing the classification of these signatures in a computationally manageable manner. The envisioned tools will be tested in (3D) CT-angiography scans and 3D-confocal microscopy images of pyramidal neurons. In the first case we wish to segment various soft tissues such as cardiac muscle, epicardial fat, lumen and calcium while in the second we wish to identify dendrites in a noisy background. The investigators aim in developing an algorithmic platform for soft-tissue segmentation based on novel 3D-data representations rather than a customized application. This research program requires the development of novel mathematical ideas both in mathematical analysis and in probability theory. These new mathematical concepts and methods will endow the envisioned algorithms with a unique ability native to human vision but not yet achieved in computer and robotic vision: the identification of structures and patterns independently of their position in the 3D-space. Indeed, tissues must be correctly identifiable by any automated image analysis system regardless of their position in the 3D-space or in the human body. A system with this ability will be able to circumscribe tissue boundaries with the same high accuracy in every direction in the 3D-space. This algorithmic platform can be adopted for a wide variety of imaging applications in medicine and biology, such as CT-angiography used to diagnose stenosis in coronary arteries or contrast CT for the detection of liver cancer. Detecting abnormalities in the walls of coronary arteries especially of their regions proximal to the ascending aorta will help prevent the most life-threatening infarctions and possibly monitor the treatment of the atherosclerotic plaque without the frequent use of the grossly invasive intravascular ultrasound probes. Identifying cancerous lesions in the liver at their early stages of development can significantly increase the chances of survival in this type of cancer. Capturing accurately the structure of dendrites and of their protruding attachments called spines in images acquired with 3D-confocal microscopes is a prime time goal as spines seem to hold the key of understanding the biological basis of depression and bipolar disorder.