Introduction and Background

Abstract

Quantitative analysis is the primary motivation for digital image processing in many applications. For example, locating and measuring anatomical structures in medical images is an important first step in diagnosis and treatment planning. In biology and other scientific disciplines, the analysis of structures in images often leads to better understanding of the underlying mechanisms in the systems being studied. Often the shape of structures within images plays an important role in this identification and analysis process. For example, we might distinguish different types of blood cells by their shape and perhaps gain an understanding of blood disorders by an analysis of shape variations. For this reason, determining how the shape of image structures should be best represented to facilitate quantitative analysis is one of the central problems in computer-aided image analysis. To answer this difficult question requires an understanding of what shape is and how shape information should be extracted from grey-scale images.