Visually-guided obstacle avoidence in unstructured environments

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 65-69).by Liana M. Lorigo.M.S

Co-dimension 2 Geodesic Active Contours for MRA Segmentation

Automatic and semi-automatic magnetic resonance angiography (MRA)s segmentation techniques can potentially save radiologists larges amounts of time required for manual segmentation and cans facilitate further data analysis. The proposed MRAs segmentation method uses a mathematical modeling technique whichs is well-suited to the complicated curve-like structure of bloods vessels. We define the segmentation task as ans energy minimization over all 3D curves and use a level set methods to search for a solution. Ours approach is an extension of previous level set segmentations techniques to higher co-dimension

Curve evolution for medical image segmentation

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 134-143).by Liana M. Lorigo.Ph.D

Visually-guided obstacle avoidance in unstructured environments

This paper presents an autonomous vision-based obstacle avoidance system. The system consists of three independent vision modules for obstacle detection, each of which is computationally simple and uses a di erent criterion for detection purposes. These criteria are based on brightness gradients, RGB (Red, Green, Blue) color, and HSV (Hue, Saturation, Value) color, respectively. Selection of which modules are used to command the robot proceeds exclusively from the outputs of the modules themselves. The system is implemented on a small monocular mobile robot and uses very low resolution images. It has been tested for over 200 hours in diverse environments. Keywords: Vision-based navigation, space exploration, modular design, reactive control, unstructured terrain.

Segmentation of Bone in Clinical Knee MRI Using Texture-Based Geodesic Active Contours

. This paper presents a method for automatic segmentation of the tibia and femur in clinical magnetic resonance images of knees. Texture information is incorporated into an active contours framework through the use of vector-valued geodesic snakes with local variance as a second value at each pixel, in addition to intensity. This additional information enables the system to better handle noise and the non-uniform intensities found within the structures to be segmented. It currently operates independently on 2D images (slices of a volumetric image) where the initial contour must be within the structure but not necessarily near the boundary. These separate segmentations are stacked to display the performance on the entire 3D structure. 1 Introduction We address the problem of automatically segmenting clinical MRI of knees. There are many applications of this capability, including diagnosis, changedetection, as a pre-cursor to registration with a model, and in the building of an initial ..

Segmentation by Adaptive Geodesic Active Contours

This paper introduces the use of spatially adaptive components into the geodesic active contour segmentation method for application to volumetric medical images. These components are derived from local structure descriptors and are used both in regularization of the segmentation and in stabilization of the image-based vector field which attracts the contours to anatomical structures in the images. Theyare further used to incorporate prior knowledge about spatial location of the structures of interest. These components can potentially decrease the sensitivity to parameter settings inside the contour evolution system while increasing robustness to image noise. We show segmentation results on blood vessels in magnetic resonance angiography data and bone in computed tomography data

Codimension-two geodesic active contours for the segmentation of tubular structures

Curve evolution schemes for segmentation, implemented with level set methods, have become an important approach in computer vision. Previous work has modeled evolving contours which are curves in 2D or surfaces in 3D. Our objective is to explore recent mathematical work enabling the evolution of manifolds of higher co-dimension. We consider 1D curves in 3D (codimension-two) for the application of automatically segmenting blood vessels in volumetric magnetic resonance angiography (MRA) images. This paper describes the theoretical foundations of our system, CURVES, then provides segmentation results compared against segmentations obtained interactively by a neurosurgeon. Segmentations of bronchi in lung computed tomography (CT) scans are also presented. The new experiments, comparisons to manual segmentations, and sample comparison to the use of a codimension-one regularization force are the primary contributions of this report