%0 Journal Article
%T Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans
%A Ayman El-Baz
%A Ahmed Elnakib
%A Mohamed Abou El-Ghar
%A Georgy Gimel'farb
%A Robert Falk
%A Aly Farag
%J International Journal of Biomedical Imaging
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/517632
%X Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200£¿CT data sets show that the proposed approach provided comparable results with respect to the experts. 1. Introduction Lung cancer remains the leading cause of cancer-related deaths in the US. In 2012, there were approximately 229,447 new cases of lung cancer and 159,124 related deaths [1]. Early detection of lung tumors (visible on chest film as nodules) may increase the patient¡¯s chance of survival, but detecting nodules is a complicated task. Nodules show up as relatively low-contrast white circular objects within the lung fields. The difficulty for computer-aided detection (CADe) schemes is distinguishing true nodules from (overlapping) shadows, vessels, and ribs. CADe systems for detection of lung nodules in thoracic CT generally consist of two major stages: (1) selection of the initial candidate nodules and then (2) elimination of the false positive nodules (FPNs) with preservation of the true positive nodules (TPNs). At the first stage, conformal nodule filtering or unsharp masking can enhance nodules and suppress other structures to separate the candidates from the background by simple thresholding or a multiple gray-level thresholding technique [2, 3]. To improve the separation, background trend is corrected in [4, 5] within image regions of interest. Then, a series of 3D cylindrical and spherical filters are used to detect small lung nodules from high-resolution CT images [6, 7]. Circular and semicircular nodule candidates can be detected by template matching [8¨C11]. However, these spherical, cylindrical, or circular assumptions are not adequate for describing the general geometry of the lesions. This is because their shape can be irregular due to the speculation or the attachments to the pleural surface (i.e.,
%U http://www.hindawi.com/journals/ijbi/2013/517632/