Parameter Estimation for Ridge Detection in Images with Thin Structures

This paper presents an analysis of four ridge detectors in images with thin structures: plant root images and retinal images. Two proposed detectors and two detectors from the literature are used. We estimate the optimal parameters for each detector for the two applications using a ROC curve similar approach. Simulated images of plant roots and retinal images are used. The optimal parameters are estimated and then used in real images. We conclude that the proposed detector based on mathematical morphology and the one based on the steerable filter are the best for both set of images.

[1]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[2]  Sylvain Berlemont,et al.  Combining Local Filtering and Multiscale Analysis for Edge, Ridge, and Curvilinear Objects Detection , 2010, IEEE Transactions on Image Processing.

[3]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Mihai Datcu,et al.  Road detection in dense urban areas using SAR imagery and the usefulness of multiple views , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  Roberto H. Bamberger,et al.  Orientation selective operators for ridge, valley, edge, and line detection in imagery , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Tony Lindeberg.  Edge Detection and Ridge Detection with Automatic Scale Selection , 2004, International Journal of Computer Vision.

[8]  Mathews Jacob,et al.  Design of steerable filters for feature detection using canny-like criteria , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Anca Dima,et al.  Automatic segmentation and skeletonization of neurons from confocal microscopy images based on the 3-D wavelet transform , 2002, IEEE Trans. Image Process..

[10]  Alejandro F. Frangi,et al.  Three-dimensional model-based analysis of vascular and cardiac images , 2001 .

[11]  Joan Serrat,et al.  Evaluation of Methods for Ridge and Valley Detection , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Sean Dougherty,et al.  Edge Detector Evaluation Using Empirical ROC Curves , 2001, Comput. Vis. Image Underst..

[13]  Edward R. Dougherty,et al.  Hands-on Morphological Image Processing , 2003 .

[14]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[15]  J. Canny.  A Computational Approach toEdgeDetection , 1986 .

[16]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[17]  John M. Gauch,et al.  Multiresolution Analysis of Ridges and Valleys in Grey-Scale Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..