Higher-order regularization and morphological techniques for image segmentation

Abstract

Image segmentation is an important field in computer vision and one of its most active research areas, with applications in image understanding, object detection, face recognition, video surveillance or medical image processing. Image segmentation is a challenging problem in general, but especially in the biological and medical image fields, where the imaging techniques usually produce cluttered and noisy images and near-perfect accuracy is required in many cases. In this thesis we first review and compare some standard techniques widely used for medical image segmentation. These techniques use pixel-wise classifiers and introduce weak pair-wise regularization which is insufficient in many cases. We study their difficulties to capture higher-level structural information about the objects to segment. This deficiency leads to many erroneous detections, ragged boundaries, incorrect topological configurations and wrong shapes. To deal with these problems, we propose a new regularization method that learns shape and topological information from training data in a non-parametric way using higher-order potentials.

Keywords

• Image Segmentation
• Random Fields
• Higher-order Models
• Geodesic Active Contours
• Mathematical Morphology