Abstract
In this paper, we present a novel adaptive B-Snake model for object contour extraction. A cubic B-Snake model is developed for extracting 2D deformable objects from medical images, with an adaptive control point insertion algorithm that is suggested to increase the flexibility of B-Snake to describe complex shape. This method overcomes the problems that exist in other B-spline based model that have to decide beforehand or exhaustively search over a range of value for the number of control points. Hence, these methods are less flexible to describe unknown complex shapes. A minimum energy method which we called Minimum Mean Square Error (MMSE) is proposed for B-Snake to push it to the target boundary. The internal forces are not required in deforming B-Snake since the representation of B-Spline has guaranteed smoothness by hard implicit constraints. The proposed B-Snake model has been tested on object contour extraction such as human brain ventricle in Magnetic Resonance (MR) images. The experimental results demonstrate the capability of adaptive shape description and object contour extraction.
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