The detection and feature extraction method of curvilinear convex regions with weak contrast using a gradient vector distribution model

In this work, we propose a new filter to detect and enhance lines in real images with varying contrast. It is robust against noise disturbances and also its output does not depend on the contrast. We first define the line-convergence vector field model based on the distribution of gradient vector orientation. Next we define a criterion index called the line-convergence degree to evaluate the likelihood of the existence of a line. The output of the proposed filter is defined as the average of line-convergence degrees in a region which is adapted to the gradient vector distribution. The filter output is a function of only gradient vector orientation and it is free from absolute intensity and relative contrast variations. Experimental results using artificial images and real images show the effectiveness of the proposed filter.

[1]  H. Kobatake,et al.  The line detection method with robustness against contrast and width variation applied in gradient vector field , 2000 .

[2]  Ying Sun,et al.  Directional low-pass filtering for improved accuracy and reproducibility of stenosis quantification in coronary arteriograms , 1995, IEEE Trans. Medical Imaging.

[3]  J. Modestino,et al.  Edge Detection in Noisy Images using Recursive Digital Filtering. , 1977 .

[4]  A. Chiang,et al.  Three‐dimensional mapping of brain neuropils in the cockroach, Diploptera punctata , 2001, The Journal of comparative neurology.

[5]  Shinichi Tamura,et al.  Semiautomatic leakage analyzing system for time series fluorescein ocular fundus angiography , 1983, Pattern Recognit..

[6]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[7]  Hidefumi Kobatake,et al.  Detection of spicules on mammogram based on skeleton analysis , 1996, IEEE Trans. Medical Imaging.

[8]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[9]  M. B. Clowes,et al.  Finding Picture Edges Through Collinearity of Feature Points , 1973, IEEE Transactions on Computers.

[10]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[11]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Guido Gerig,et al.  3D Multi-scale line filter for segmentation and visualization of curvilinear structures in medical images , 1997, CVRMed.

[13]  Dr. M. G. Worster.  Methods of Mathematical Physics , 1947, Nature.

[14]  Norberto F. Ezquerra,et al.  Model-guided labeling of coronary structure , 1998, IEEE Transactions on Medical Imaging.

[15]  Robert M. Haralick,et al.  Ridges and valleys on digital images , 1983, Comput. Vis. Graph. Image Process..

[16]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[17]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[18]  Rita Cucchiara,et al.  The vector-gradient Hough transform for identifying straight-translation generated shapes , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[19]  Richard Szeliski,et al.  Tracking with Kalman snakes , 1993 .

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

[21]  Petros Maragos,et al.  Pattern Spectrum and Multiscale Shape Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Werner Frei,et al.  Fast Boundary Detection: A Generalization and a New Algorithm , 1977, IEEE Transactions on Computers.

[23]  Fred M. Dickey,et al.  An Optimal Frequency Domain Filter for Edge Detection in Digital Pictures , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  W. Clem Karl,et al.  A new model-based technique for enhanced small-vessel measurements in X-ray cine-angiograms , 2000, IEEE Transactions on Medical Imaging.