Segmentation of Lung in Chest Radiographs Using Hull and Closed Polygonal Line Method

Abstract

Accurate lung segmentation in chest radiographs is a challenging problem due to the presence of strong edges at the rib cage and clavicle, the varying appearance in the upper clavicle bone region, too small costophrenic angle and the lack of a consistent anatomical shape among different individuals. In this paper, we propose a hybrid semi-automatic method called Hull-Closed Polygonal Line Method (Hull-CPLM) to detect the boundaries of the lung Region of Interest (ROI). To the best of our knowledge, this is the first attempt at lung segmentation using the Hull-CPLM in chest radiographs. The proposed method has two main steps: 1) an image preprocessing method is constructed to implement the coarse segmentation by using as low as 15% of the manually delineated points as the initial points, 2) a refinement step is used to fine-tune the segmentation results based on the improved principal curve model and the machine learning model at the refinement step. To prove the performance of the proposed method, both the private and public databases were used. The private database is used to select the optimal parameters for the proposed method, where the result showed a good performance with the Dice Similarity Coefficient (DSC) as high as 97.08%. While on the public databases, our proposed algorithm not only surpassed the performance of different hybrid algorithms but also reached superior segmentation results by comparing with state-of-the-art methods.INDEX TERMS Lung segmentation, chest radiographs, principal curve, closed polygonal line method, database, machine learning.

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

“…Lung segmentation using Hull-CPLM [2] Selects the ROI for lung detection Preprocessing is required Nongrid registration lung segmentation [25] Sift-flow modeling for registration provides…”

“…The automatic segmentation of the chest anatomy is important for diagnosing pulmonary diseases, where the radiologist evaluates pulmonary discrepancies, such as nodules, lung deformation, and tissue mass disorders [1]. The chest X-Ray (CXR) is used world-wide for the chest analysis of several diseases, including pulmonary cancer, which is the leading cause of death [2]. The CXR is a common diagnostic tool used by doctors to detect various radiological signs.…”

“…Several studies have evaluated segmentation of the chest anatomy to estimate the CTR for cardiomegaly and related diseases [9][10][11][12][13]. To obtain advancement in diagnosis, automated systems are required to aid the medical specialist and overcome the diagnostic burden [2,3]. Most of the described diseases are related to the shape and size of the anatomical structures, which require accurate segmentation of the lung and heart boundaries.…”

“…Lung segmentation benefits the diagnosis of diseases, such as cardiomegaly, emphysema, pleural effusion, etc., where heart segmentation can be used to determine the cardiothoracic ratio [1]. Automatic pulmonary disease detection using computer-aided diagnosis (CAD) is based on the correct segmentation of anatomical structures, such as the lungs, heart, and clavicle bones [2]. With the success of deep learning, artificially intelligent algorithms can help medical experts and ophthalmologists to detect and diagnose the disease and increase diagnostic throughput [14][15][16][17][18][19][20].…”

“…Anatomical structure segmentation of the chest can be divided into two groups of conventional handcrafted features and deep feature-based methods. Starting from the baseline of handcrafted features-based methods that just consider the single class lung segmentation [2] using local features, researchers have mainly focussed on the general image processing-based methods for the chest anatomy segmentation, as presented in studies [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. As this study is based on multiclass deep learning-based semantic segmentation, we mainly focus on learned feature-based literature.…”