# Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model
## 1 Introduction  
Human lungs consist of five parts, separated by pulmonary fissures. In CT images, pulmonary fissures often appear as bright thin curvilinear shapes in 2D space or plate-like structures in 3D space [1]. For clinical diagnosis, some postoperative cardiopulmonary complications can be predicted by the degree of fissure completeness [2]. However, pulmonary fissure detection in CT images is not an easy task due to complex factors like the thin membrane shape, low contrast, intensity variety, pathological deformation and partial volume effect [3].  
In recent years, many valuable studies have been presented on pulmonary fissure detection, most of which were designed by identifying the points of interest, the curved lines or the plate-like structures in CT images to achieve detection. To identify the points of interest in CT images, [PERSON] et al. [4] proposed an automatic and robust algorithm for pulmonary fissure segmentation in CT images. To better suppress tubular structures, [PERSON] et al. [5], [PERSON] et al. [6] and [PERSON] et al. [7] added one or more tubular structure inhibitors. Using a different strategy, [PERSON] et al. [8] introduced a modified ant colony optimization framework to identify lobe fissures. Because the prior linear and planar shape features are not taken into account, these approaches may partly account for missed fissures.  
Motivated by a fact that pulmonary fissures appear like curved lines in the 2D cross-section of CT scans, [PERSON] et al. [9] presented a template matching method to segment pulmonary fissures. Similarly, [PERSON] et al. [10] used a ridgeness measure model to enhance pulmonary fissures, then a fuzzy reasoning system was applied to highlight pulmonary fissure representation. [PERSON] et al. [11] used a ridge map method to enhance the major fissures and applied a Bayesian network model to segment pulmonaryfissures. However, these methods [10; 11] generally need some manual interaction. To automatically extract pulmonary fissures and overcome the above disadvantages [10; 11], a line enhancing filter [12] was designed for pulmonary fissure segmentation. Recently, [PERSON] et al. [13] proposed a derivative of stick filter (DoS) to enhance pulmonary fissures and designed a post-processing pipeline for pulmonary fissure segmentation. However, this approach utilized only the magnitude information in CT images, ignoring orientation information. Its improved version [14] presented a novel approach by integrating magnitude information and orientation information to segment pulmonary fissures. Assuming the pulmonary fissure to be a plate-like surface in 3D space, [PERSON] et al. [15] presented a computational geometry model to extract pulmonary fissures. Their later improvement [16] included a piecewise plane model to locally fit the fissure surface, but the model could deviate from the abnormality of fissures in real images, thus resulting in missed detection.  
In this paper, a novel scheme is proposed for fissure detection in CT images. Following the idea of previous work [13; 14], we utilized the 2D curvilinear shape feature to detect the fissure lines across a series of 2D images. In contrast to the existing work, a multiple section model with multiple planes cutting through the 3D volume image was introduced for fissure profile detection. This was expected to avoid the missed detection of fissure patches located along specific section planes. Moreover, an improved orientation partition scheme was developed to separate the plate-like fissure surface from adhering clutters, and finally refine the fissure segmentation result.