A HYBRID APPROACH FOR AUTOMATED DETECTION OF LUNG NODULES IN CT IMAGES J. DEHMESHKI, X. YE, M. V. CASIQUE, XY. LIN Medicsight PLC, 46 Berkeley square, London, WIJ 5AT, UK Abstract This paper presents a novel shape based Genetic Algorithm Template Matching (GATM) method for the automated detection of lung nodules. The GA process is employed as an optimisation method to effectively search for the location of nodule candidates within the lung area. To define the fitness function for GATM, 3D geometric shape feature is calculated at each voxel and then combined into global nodule intensity distribution. Lung nodule phantom images are used as reference images for template matching. The proposed method has been validated on 70 clinical thoracic CT scans that contain 178 nodules as a gold standard. 151 nodules were detected by the proposed method, a detection rate of 85%, with the number of False Positives (FP) at approximately 14.0/scan. This high detection performance provides a good basis for a Computer-Aided Detection (CAD) system for lung nodules. 1. INTRODUCTION Lung cancer is the most common cause of cancer death [1]. Early detection and treatment of lung cancer can significantly improve the long term health of those inflicted with it. Nodules can be missed due to low relative contrast, small size, or location of the nodule within an area of complicated anatomy. Recently, researchers have developed a number of computer-aided lung nodule detection methods to aid radiologists in identifying nodule candidates from CT images. The approaches can be divided into two groups: intensity based [2, 3] and model based detection methods [4, 5, 6]. Although much of the effort was devoted to the Computer- Aided Detection (CAD) of lung nodules, lung CAD system still remains an ongoing research task and should be improved further [7]. One of the major difficulties that should be tackled is to detect nodules which are adjacent to anatomical structures such as blood vessels or the chest wall when they have very similar X-ray attenuation and appearance in individual cross-sectional CT images or to detect nodules which are in non-spherical shapes. To tackle this problem, a new hybrid approach has been developed which is based on the shape-based Genetic Algorithm Template Matching (GATM). 3D local shape information is combined into global nodule intensity distribution for fitness calculation of GA process. Furthermore, new definition for chromosome is proposed which includes directional information. Lung nodule phantom images are used as references for template matching instead of synthetic Gaussian template suggested in [4]. From the experimental results shown in section 3, the proposed method is robust to the templates, and also is able to detect non- spherical nodules with local spherical elements. Details of proposed method are described in following sections. 2. METHOD As it is well known, a typical CAD system for lung nodule detection consists of three major phases. The first phase deals with detection of all potential nodules (objects). Then important features of each object will be extracted in second phase. The extracted features, in third stage, are incorporated into a classifier to reduce FP objects (normal tissues). The overall performance of a CAD system depends on performances of each individual phase. Typically, the most challenging aspect of a CAD system is the first phase, object detection. In this paper, we focus on the first phase aiming at developing a method to detect most of the nodules candidates while introducing only a few FP objects into next phases. Figure 1 provides a flow diagram outlining the key steps in the proposed approach. The lung area is firstly extracted by using an adaptive thresholding method followed with a rolling ball algorithm [8]. Further processing of nodule candidates detection is carried out in the segmented lung region. Rules based filtering is then used to remove easily dismissible FP such as joint of vessels. The main focus of this study, shape based GATM, is shown in boldface in the figure. CT Lung Image Lung Extraction Shape-based GATM Process Rules-based Filtering for FP Reduction Nodule candidates Figure 1 Flow diagram of proposed nodule detection system 1.1. Shape based GA template matching The GATM process is used as an optimisation method to determine the target position of the nodule candidates within the lung area. Compared to linear searching, the advantage of using GA searching method is its stochastic optimisation characteristics, which simulates the evolution processes such as natural selection and the genetic modifications. Three key issues in proposed shape based GATM are: (a) how to define fitness function considering the shape information and nodule intensity distribution; (b) how to design the chromosome; and (c) how to create template images. In the following sections, 506 0-7803-9577-8/06/$20.00 ©2006 IEEE ISBI 2006