* besma@utk.edu; phone 865 974-9918; fax 865 974-5459 Grayscale enhancement techniques of x-ray images of carry-on luggage Besma Abidi*, Mark Mitckes, Mongi Abidi, and Jimin Liang University of Tennessee – Imaging, Robotics, and Intelligent Systems Laboratory 317 Ferris Hall, Knoxville, TN 37996-2100 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. Keywords: luggage scenes, threat detection, x-ray, image enhancement, decluttering, auto-thresholding, cluster validity 1. INTRODUCTION Luggage inspection is an essential process for airports and airplane security due to the presence of large crowds (customers and personnel) and a history of terrorists’ patterns in airports and on airplanes. On the other hand, luggage inspection has always been a challenge due to the complexities present in knowing the content of each individual bag. The drastic growth in various technologies has also led to an increase in the level of sophistication and methods of device concealment by terrorists. The problems are compounded by considerations of a screener constantly gazing at a screen and seeing almost the same type of objects over and over again. By automating or semi-automating the process of inspection at carry-on luggage stations, an increase in operator alertness, inspection speed, and customer convenience will accrue. This automation process, even at very low levels, entails processing images, highlighting items, de-cluttering scenes, and cleverly displaying information. Intensity manipulation is used to generate richer, brighter, clearer, and cleaner versions of an image. Images’ gray-level distributions are modified using a number of common and newly developed image enhancement techniques for the purpose of increasing contrast and adjusting brightness to make the various components of the luggage scene more distinct and reduce the amount of clutter in the image. Common enhancement techniques are first applied to x-ray luggage scenes and results shown in Section 2. Section 3 deals with a novel image decluttering method, the image hashing algorithm, its variations, and its applications to x-ray luggage images. In Section 4, automation, via the use of a newly developed metric, of various aspects of the hashing algorithm and comparison to other cluster validity measures are presented. 2. APPLICATION OF COMMON ENHANCEMENT TECHNIQUES TO LUGGAGE SCENES In this section, common image enhancement methods are applied to raw x-ray data of luggage scenes and results generated in an effort to demonstrate the valuable support image processing techniques could bring to screeners and the performance improvement that will ensue from incorporating these techniques in luggage inspection tasks. 2.1. Linear regression The first basic procedure applied to x-ray luggage data is linear regression which provides for the “stretching” of the pixel range within a given image so that the maximum and minimum pixel values cover a wider range (or all of the range), providing an enhanced image from the original . Linear regression can be mathematically formulated by equation (1) (1) , ) , ( ) , ( b y x f a y x g + × = 149