Extraction of Local Structural Features in Images by Using a Multi-scale Relevance Function
Extraction of structural features in radiographic images is considered in the context of flaw detection with application to industrial and medical diagnostics. The known approache, like the histogram-based binarization yield poor detection results for such images, which contain small and low-contrast objects of interest on noisy background. In the presented model-based method, the detection of objects of interest is considered as a consecutive and hierarchical extraction of structural features (primitive patterns) which compose these objects in the form of aggregation of primitive patterns. The concept of relevance function is introduced in order to perform a quick location and identification of primitive patterns by using the binarization of regions of attention. The proposed feature extraction method has been tested on radiographic images in application to defect detection of weld joints and extraction of blood vessels in angiography.
Unable to display preview. Download preview PDF.
- 1.A. Kehoe and G. A. Parker. Image processing for industrial radiographic inspection: image inspection, British Journal of NDT, Vol. 32, N. 4, pp. 183–190, 1990.Google Scholar
- 5.T. W. Liao and J. Ni. An automated radiographic NDT system for weld inspection: Part I Weld extraction, NDT&EInternational, Vol. 29, No. 3, pp. 157–162, 1996.Google Scholar
- 7.S. Mallat. A theory for multi-resolution image signal decomposition: the wavelet representation, IEEE Trans., Vol. PAMIit11, pp. 674–693, 1989.Google Scholar
- 12.M. D. Wheeler and K. Ikeuchi. Sensor modeling, probabilistic hypothesis generation, and robust localization for object recognition, IEEE Trans. Vol. PAMI-17, No. 3, pp. 252–266, 1995.Google Scholar
- 13.A. B. Frakt, W.C. Karl and A. S. Willsky. A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data, IEEE Trans. Image Proc., Vol. 7, No. 6, pp. 404–420, 1998.Google Scholar
- 14.R. M. Palenichka and U. Zscherpel. Detection of flaws in radiographic images by hypothesis generation and testing approach, Proc. Int. Workshop Advances in Signal Processing for NDE of Materials, pp. 235–243, 1998.Google Scholar
- 15.A. Rosenfeld and S. Banerjee. Maximumlikelihood edge detection in digital signals, CVGIP: Image Understanding, Vol. 55, No. 1, 1992.Google Scholar
- 16.L.P. Kaebling and A. W. Moore. Reinforcement learning: a survey, Journal of Artificial Intelligence Research, No. 4, pp. 237–285, 1996.Google Scholar
- 17.P. Perner, T. P. Belikova and N. I. Yashunskaya. Knowledge acquisition by symbolic decision tree induction for interpretation of digital images in radiology, Proc. Int. Workshop Structural and Syntactic Pattern Recognition, pp. 208–220, 1996.Google Scholar
- 20.P. Perner and W. Paetzold. An incremental learning system for interpretation of images, Proc. Int. Workshop Shape, Structure and Pattern Recognition, pp. 311–319, 1994.Google Scholar
- 22.L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis, IEEE Trans., Vol. PAMI 20, No. 11, pp. 1254–1259, 1998.Google Scholar
- 23.T. C. Folsom and R. B. Pinter. Primitive features by steering, quadrature, and scale, IEEE Trans., Vol. PAMI 20, No. 11, pp. 1161–1173, 1998.Google Scholar