# Lung Field Segmentation in Chest X-rays:
## 1 Introduction
Ultimately, our method aims to accurately segment the overall lung regions, benefiting from a very sparse localization of rib cage points, which belong to an imprecise initial contour approximation. In the same manner as our method, [PERSON] et al. avoided any training, opting for a straightforward sequential, rule-based algorithm [22]. They highlighted the fact that an unsupervised method is mandatory for a robust Content-Based Medical Image Retrieval System (CBMIRS). Preprocessing deals with different looking X-rays due to different machine configurations, while they managed to estimate the lung border by automatically selecting threshold values for each direction. However, the elimination of clavicles, sides and body artifacts is highly parametric, a fact that increases the risk of low generalization capability. The initial mask is optimized thanks to a clustering approach. Our method can achieve a particularly good quality segmentation result with much fewer parameters since it principally exploits morphological structure information in and around the lungs, which in most cases remains invariable across CXR images. A notable lung shape deformation appears in radiographs from patients suffering from bacterial pulmonary infections, where the lung boundary might not be determinable even by an experienced observer. This specific application was the focus of [PERSON] et al., who studied CXRs obtained using portable devices with problematic positioning of the patient [23]. They utilized a robust algorithm for the detection of several salient points in the outer rib cage and the spinal cord, which also assisted in a selective thresholding procedure for the different parts. Finally, as done by many other researchers in the field of CXR segmentation, they employed an active shape model (ASM) approach to build a shape prior to their overall algorithm, based on ground truth lung field boundaries. The fact that shape learning is a kernel part in ASM can be a limiting factor in cases where a scarcity of training data that cover a broad range of shape variations is present. An active shape model (ASM) based on gradient vector flow was applied by [PERSON] et al. [24]. Given a test image, an initial contour estimate is achieved, using a point distribution model. By introducing a global gradient vector flow, the contour is attracted towards the object boundary. Finally, in the method of [PERSON] et al. [25], the aim is to drive the contour to a given binary prior shape. They emphasized the challenges that are introduced by local minima due to shading effects, by non-rigid shape variations of the lung and by the undesirable presence of unrelated strong edges. In their method, the aim is to drive the contour to a given binary prior shape. The generation of the initial seed mask is considered very crucial and is completed based on two levels of thresholding. After the identification of the rib cage points, our algorithm tries to grow the initial segmented lung area towards the actual border of the lung. The border is automatically identified in an unsupervised manner, around the rib cage points. Hence, in comparison to the previously mentioned ASM-based methods, it does not depend on the availability of labeled training data, while it demonstrates a reduced risk of falsely identified border points due to the limited search area. The available literature, which is reviewed above, is summarized in Tables 1 and 2.