This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm3, volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

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Our method was shown to improve segmentation accuracy for several specific challenging cases. The results demonstrate that our approach achieved a superior accuracy over two state-of-the-art methods.

In this study, a technique for computer-aided diagnosis (CAD) systems to detect lung nodules in X-ray pulmonary computed tomography (CT) images is proposed. The adaptive border marching algorithm was implemented for lung volume segmentation. Region growing and rule based method were used to detect the nodules candidates. Then, we extracted a total of 11 features, including intensity features and geometry features, of these candidates. The fuzzy min-max neural network classifier with compensatory neurons (FMCN) was advanced by K-means clustering, for false-positive reduction. In hyper-space, the cluster is similar to hyperbox, thus the K-means clustering algorithm was implemented for determine the expansion coefficient (hyperbox size). Nineteen clinical cases involving a total of 5766 slice images were used in this study. 26 nodules out of 31 were detected by our CAD (the sensitivity about 84%), with the number of false-positive at approximately 2.6 per CT scan. The preliminary results show that our scheme can be regarded as a potential technique for CAD systems to detect nodules in pulmonary CT images. Keywords-Computer-aided diagnosis; lung nodules; K-means cluster; fuzzy min-max neural networkI.

Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.