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Automated Explosive Detection Systems (EDS) utilizing Computed Tomography (CT) generate a series of 2-D projections from a series of X-ray scans OF luggage under inspection. 3-D volumetric images can also be generated from the collected data set. Extensive data manipulation of the 2-D and 3-D image sets for detecting the presence of explosives is done automatically by EDS. The results are then forwarded to human screeners for final review. The final determination as to whether the luggage contains an explosive and needs to be searched manually is performed by trained TSA (Transportation Security Administration) screeners following an approved TSA protocol. The TSA protocol has the screeners visually inspect the resulting images and the renderings from the EDS to determine if the luggage is suspicious and consequently should be searched manually. Enhancing those projection images delivers a higher quality screening, reduces screening time and also reduces the amount of luggage that needs to be manually searched otherwise. This paper presents a novel edge detection algorithm that is geared towards, though not exclusive to, automated explosive detection systems. The goal of these enhancements is to provide a higher quality screening process while reducing the overall screening time and luggage search rates. Accurately determining the location of edge pixels within 2-D signals, often the first step in segmentation and recognition systems indicates the boundary between overlapping objects in a luggage. Most of the edge detection algorithms such as Canny, Prewitt, Roberts, Sobel, and Laplacian methods are based on the first and second derivatives/difference operators. These operators detect the discontinuities in the differences of pixels. These approaches are sensitive to the presence of noise and could produce false edges in noisy images. Including large scale filters, may avoid errors generated by noise, but often simultaneously eliminating the finer edge details as well. This paper proposes a novel pixels ratio based edge detection algorithm which is immune to noise. The new method compares ratios of pixels in multiple directions to an adaptive threshold to determine edges in different directions.
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