Deformable models for image segmentation: A critical review of achievements and future challenges

Abstract

Image segmentation is a fundamental and tedious task of computer vision. Because of inherent noise and intensity inhomogeneity in real-world images, it remains a difficult problem in practical applications such as image analysis, scene understanding, object detection, and many others. Several mathematical models proposed for image segmentation in the past few decades with an effective policy. Among these models, deformable models earned more attention and are widely used techniques due to their accuracy, efficiency, and robust effectiveness. This study reviews and compares various deformable models for the segmentation of digital images available in the literature. First, for a comprehensive study, these deformable models are classified into two classes such as direct partial differential equation (PDE) based approaches and variational based approaches. Beside this, variational based approaches are further classified into parametric and geometric models. Their advantages as well as shortcomings are discussed in detail from an objective viewpoint. Then, to check the robustness of various discussed classes of deformable models, a set of synthetic, natural, and real medical images are considered along with inhomogeneity and noise. Also, to measure the segmentation accuracy, different quantitative metrics based on contour and region are utilized. Numerical experiments with different classes of images, reveal that both direct PDE based and variation based models perform well in clean and noisy images. Whereas, the variational based approaches are superior for images having intensity inhomogeneity with high computational time. The qualitative and quantitative investigations confirm that the subtle change in model assumptions can have a significant impact on segmentation.