Normal estimation is a crucial first step for numerous light detection and ranging (LiDAR) data-processing algorithms, from building reconstruction, road extraction, and ground-cover classification to scene rendering. For LiDAR point clouds in urban environments, this paper presents a robust method to estimate normals by constructing an octree-based hierarchical representation for the data and detecting a group of large enough consistent neighborhoods at multiscales. Consistent neighborhoods are mainly determined based on the observation that an urban environment is typically comprised of regular objects, e.g., buildings, roads, and the ground surface, and irregular objects, e.g., trees and shrubs; the surfaces of most regular objects can be approximatively represented by a group of local planes. Even in the frequent presence of heavy noise and anisotropic point samplings in LiDAR data, our method is capable of estimating robust normals for kinds of objects in urban environments, and the estimated normals are beneficial to more accurately segment and identify the objects, as well as preserving their sharp features and complete outlines. The proposed method was experimentally validated both on synthetic and real urban LiDAR datasets, and was compared to state-of-the-art methods.

To achieve an automatic and accurate segmentation of lungs and improve the clinical efficiency of computer‐aided diagnosis, the authors present a lung segmentation algorithm based on the random forest method and a multi‐scale edge detection technique. The algorithm carries a first step of lung region extraction and a second step of lung nodule segmentation. By combining texture information, the improved superpixel generation method can better deal with initial segmentation on lung computed tomography images with inhomogeneous intensity. Then, the lung region is further extracted by using the random forest classifier on the superpixel features, and the lung contours are corrected with a proposed circle tracing technique. Finally, the segmentation is further refined by employing a multi‐scale edge detection technique, which enables their method to detect suspicious nodules with various intensities and sizes adaptively. The effectiveness of the proposed approach is demonstrated on a group of datasets by comparing with the corresponding ground truths as well as the classical algorithms. Experimental results show that the proposed method has a higher precision than the compared algorithms in a fully automatic fashion.

Lung segmentation, a prerequisite step of lung disease detection in computer‐aided diagnosis system, is a challenging task because of noises, complex structures, as well as large individual differences of lung CT scans. Here, an automatic algorithm for segmenting lungs from thoracic CT images accurately is presented. This scheme consists of three principal steps: image preprocessing, lung extracting and contour correcting. To cope with inhomogeneous intensities of CT images, a novel preprocessing approach based on empirical mode decomposition and bilateral filter is proposed, which has abilities of denoising, smoothing and edge keeping. Lung region is then extracted with a novel gray correlation‐based clustering approach. A new lung contour correction technology is finally employed to repair the concave regions caused by pulmonary nodules, vessels and so on. Experimental results show that the preprocessing approach outperforms other methods on image denoising and smoothing. Meanwhile, the lung segmentation algorithm is tested on a group of lung CT images affected with interstitial lung diseases and achieves a high segmentation accuracy. Compared with several existing lung segmentation methods, this algorithm exhibits a better performance on lung segmentation.