Image segmentation using fuzzy energy-based active contour with shape prior

• A fuzzy energy-based active contour model with shape prior information for image segmentation is proposed.
• The pose variation and energy minimization problems are handled without solving Euler–Lagrange equations.
• The evolving shape and reference shape are aligned using a shape normalization procedure.
• The reference shape could be constructed by performing the Principle Component Analysis on a set of training shapes.
• The energy functional is minimized by directly calculating the fuzzy energy alterations.

This paper presents a fuzzy energy-based active contour model with shape prior for image segmentation. The paper proposes a fuzzy energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach proposed by Chan and Vese, evolves the contour relied on image information. The shape term inspired from Chan and Zhu’s work, defined as the distance between the evolving shape and a reference one, constrains the evolving contour with respect to the reference shape. To align the shapes, we exploit the shape normalization procedure which takes into account the affine transformation. In addition, to minimize the energy functional, we utilize a direct method to calculate the energy alterations. The proposed model therefore can deal with images with background clutter and object occlusion, improves the computational speed, and avoids difficulties associated with time step selection issue in gradient descent-based approaches.