Xinyu Lin

Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass opacity (GGO) nodules (part solid and nonsolid). This method consists of several steps. First, the lung region is segmented from the CT data using a fuzzy thresholding method. Then, the volumetric shape index map, which is based on local Gaussian and mean curvatures, and the ldquodotrdquo map, which is based on the eigenvalues of a Hessian matrix, are calculated for each voxel within the lungs to enhance objects of a specific shape with high spherical elements (such as nodule objects). The combination of the shape index (local shape information) and ldquodotrdquo features (local intensity dispersion information) provides a good structure descriptor for the initial nodule candidates generation. Antigeometric diffusion, which diffuses across the image edges, is used as a preprocessing step. The smoothness of image edges enables the accurate calculation of voxel-based geometric features. Adaptive thresholding and modified expectation-maximization methods are employed to segment potential nodule objects. Rule-based filtering is first used to remove easily dismissible nonnodule objects. This is followed by a weighted support vector machine (SVM) classification to further reduce the number of false positive (FP) objects. The proposed method has been trained and validated on a clinical dataset of 108 thoracic CT scans using a wide range of tube dose levels that contain 220 nodules (185 solid nodules and 35 GGO nodules) determined by a ground truth reading process. The data were randomly split into training and testing datasets. The experimental results using the independent dataset indicate an average detection rate of 90.2%, with approximately 8.2 FP/scan. Some challenging nodules such as nonspherical nodules and low-contrast part-solid and nonsolid nodules were identified, while most tissues such as blood vessels were excluded. The methods high detection rate, fast computation, and applicability to different imaging conditions and nodule types shows much promise for clinical applications.

Automated detection of lung nodules in CT images using shape-based genetic algorithm.

A shape-based genetic algorithm template-matching (GATM) method is proposed for the detection of nodules with spherical elements. A spherical-oriented convolution-based filtering scheme is used as a pre-processing step for enhancement. To define the fitness function for GATM, a 3D geometric shape feature is calculated at each voxel and then combined into a global nodule intensity distribution. Lung nodule phantom images are used as reference images for template matching. The proposed method has been validated on a clinical dataset of 70 thoracic CT scans (involving 16,800 CT slices) that contains 178 nodules as a gold standard. A total of 160 nodules were correctly detected by the proposed method and resulted in a detection rate of about 90%, with the number of false positives at approximately 14.6/scan (0.06/slice). The high-detection performance of the method suggested promising potential for clinical applications.

Medical Image Segmentation

Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms. In this paper, image segmentation algorithms are divided into classical image segmentation algorithms and segmentation methods combined with certain mathematical tools, including threshold segmentation methods, image segmentation algorithms based on the edge, image segmentation algorithms based on the region, image segmentation algorithms based on artificial neural network technology, image segmentation algorithms based on contour model and image segmentation algorithm based on statistical major segmentation algorithm and so on. Finally, the development trend of medical image segmentation algorithms is discussed.

Efficient Computer-Aided Detection of Ground-Glass Opacity Nodules in Thoracic CT Images

In this paper, an efficient compute-aided detection method is proposed for detecting ground-glass opacity (GGO) nodules in thoracic CT images. GGOs represent a clinically important type of lung nodule which are ignored by many existing CAD systems. Anti-geometric diffusion is used as preprocessing to remove image noise. Geometric shape features (such as shape index and dot enhancement), are calculated for each voxel within the lung area to extract potential nodule concentrations. Rule based filtering is then applied to remove false positive regions. The proposed method has been validated on a clinical dataset of 50 thoracic CT scans that contains 52 GGO nodules. A total of 48 nodules were correctly detected and resulted in an average detection rate of 92.3%, with the number of false positives at approximately 12.7/scan (0.07/slice). The high detection performance of the method suggested promising potential for clinical applications.

A hybrid approach for automated detection of lung nodules in CT images

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

Automated detection of nodules in the CT lung images using multi-modal genetic algorithm

This study deploys a multi-modal genetic algorithm (GA) augmented by an island model cooperating with a speciation module to identify lung nodules in chest CT images. The genetic algorithm is a model of machine learning which derives its behaviour from the processes of evolution in nature. The island model based GA maintains diversity and converges towards different solutions hence capturing multiple peaks of the fitness function. The speciation module gathers the genetically similar individuals into one pool to avoid accumulation of several subpopulations around the same peak of the fitness function. The detection process comprises two stages. In the first stage, the template matching based GA was used to determine the target position in the observed image efficiently and to select an adequate template image from several reference patterns for quick template matching. The fitness of the individual (chromosome) was defined as the similarity calculated by the cross correlation coefficient between the target and template image as determined by the chromosome. In the second stage, the GA scheme was used to detect the regions with circular elements present in the segmented CT images as potential nodule. The fitness function of GA process was defined using three points, from the boundary of the object, given by the chromosome. The results show that the scheme can be efficiently applied for detection of isolated or attached circular regions present in the images.