Grayscale enhancement techniques of x-ray images of carry-on luggage

Besma Abidi; Mark Mitckes; Mongi A. Abidi; Jimin Liang

doi:10.1117/12.515228

1 May 2003 Grayscale enhancement techniques of x-ray images of carry-on luggage

Besma Abidi, Mark Mitckes, Mongi A. Abidi, Jimin Liang

Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.515228
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States

Abstract

Very few image processing applications dealt with x-ray luggage scenes in the past. In this paper, a series of common image enhancement techniques are first applied to x-ray data and results shown and compared. A novel simple enhancement method for data de-cluttering, called image hashing, is then described. Initially, this method was applied using manually selected thresholds, where progressively de-cluttered slices were generated and displayed for screeners. Further automation of the hashing algorithm (multi-thresholding) for the selection of a single optimum slice for screener interpretation was then implemented. Most of the existing approaches for automatic multi-thresholding, data clustering, and cluster validity measures require prior knowledge of the number of thresholds or clusters, which is unknown in the case of luggage scenes, given the variety and unpredictability of the scene’s content. A novel metric based on the Radon transform was developed. This algorithm finds the optimum number and values of thresholds to be used in any multi-thresholding or unsupervised clustering algorithm. A comparison between the newly developed metric and other known metrics for image clustering is performed. Clustering results from various methods demonstrate the advantages of the new approach.

Citation Download Citation

Besma Abidi, Mark Mitckes, Mongi A. Abidi, and Jimin Liang "Grayscale enhancement techniques of x-ray images of carry-on luggage", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); https://doi.org/10.1117/12.515228

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