Detection of local objects in radiographic images by structural hypothesis-testing approach

Roman M. Palenichka; Peter Zinterhof

doi:10.1117/12.290276

14 October 1997 Detection of local objects in radiographic images by structural hypothesis-testing approach

Roman M. Palenichka, Peter Zinterhof

Proceedings Volume 3167, Statistical and Stochastic Methods in Image Processing II; (1997) https://doi.org/10.1117/12.290276
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Detection and binary segmentation of low-contrast flaws (defects) in noisy radiographic images is considered with an application to non-destructive evaluation of materials and industrial articles. The known approaches, like the edge detection or unsharp masking with a consecutive thresholding operation, yield poor results for such images. In the presented method of object detection, a model-based approach is adopted which relies on shape constraints of the objects to be detected as well as exploits the image multiresolution representation. For detection of local objects, the maximum likelihood principle and statistical hypothesis testing is used with the confidence control during all stages of the image analysis. The proposed novel procedure of estimation of the image intensity from noisy pixels ensures a robust evaluation of basic model parameters in the presence of outliers which are considered as impulsive noise.

Citation Download Citation

Roman M. Palenichka and Peter Zinterhof "Detection of local objects in radiographic images by structural hypothesis-testing approach", Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); https://doi.org/10.1117/12.290276

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