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Unsupervised contour representation and estimation using B-splines and a minimum description length criterion.

M T Figueiredo¹, J N Leitão, A K Jain

¹Inst. Superior Tecnico, Inst. de Telecomunicaoes, Lisbon, Portugal. mtf@lx.it.pt

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 8, 2008

Summary

This study introduces an adaptive method for estimating deformable contours using B-splines. The approach automatically adjusts contour complexity and estimates image model parameters for unsupervised contour estimation.

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Area of Science:

Computer Vision
Image Analysis
Computational Geometry

Background:

Parametric deformable contours are widely used in image analysis for object segmentation.
Traditional methods often require manual specification of contour complexity or image model parameters.
B-spline representations offer flexibility in contour modeling.

Purpose of the Study:

To develop a novel, unsupervised approach for adaptive estimation of parametric deformable contours.
To integrate B-spline representations with a statistical framework for robust contour fitting.
To enable automatic estimation of contour complexity and image model parameters.

Main Methods:

Formulation of the problem within a statistical framework using a region-based image model.
Estimation of B-spline parameterization order (number of control points) using a Minimum Description Length (MDL) criterion.
Development of a deterministic iterative algorithm for contour and image model parameter estimation.

Main Results:

An unsupervised parametric deformable contour that adapts its smoothness/complexity.
Simultaneous estimation of contour parameters and image model parameters.
Successful validation on synthetic and real medical images, demonstrating adequate and good performance.

Conclusions:

The proposed B-spline based deformable contour method offers an effective unsupervised approach for image segmentation.
The adaptive estimation of contour complexity and image model parameters enhances robustness and reduces manual intervention.
The method shows significant potential for applications in medical image analysis and other computer vision tasks.