Objective: chest computed tomography (CT) images and their quantitative analyses have become increasingly important for a variety of purposes, including lung parenchyma density analysis, airway analysis, diaphragm mechanics analysis, and nodule detection for cancer screening. Lung segmentation is an important prerequisite step for automatic image analysis. We propose a novel lung segmentation method to minimize the juxta-pleural nodule issue, a notorious challenge in the applications. Method: we initially used the Chan–Vese (CV) model for active lung contours and adopted a Bayesian approach based on the CV model results, which predicts the lung image based on the segmented lung contour in the previous frame image or neighboring upper frame image. Among the resultant juxta-pleural nodule candidates, false positives were eliminated through concave points detection and circle/ellipse Hough transform. Finally, the lung contour was modified by adding the final nodule candidates to the area of the CV model results. Results: to evaluate the proposed method, we collected chest CT digital imaging and communications in medicine images of 84 anonymous subjects, including 42 subjects with juxta-pleural nodules. There were 16 873 images in total. Among the images, 314 included juxta-pleural nodules. Our method exhibited a disc similarity coefficient of 0.9809, modified hausdorff distance of 0.4806, sensitivity of 0.9785, specificity of 0.9981, accuracy of 0.9964, and juxta-pleural nodule detection rate of 96%. It outperformed existing methods, such as the CV model used alone, the normalized CV model, and the snake algorithm. Clinical impact: the high accuracy with the juxta-pleural nodule detection in the lung segmentation can be beneficial for any computer aided diagnosis system that uses lung segmentation as an initial step.

Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach

In this paper, a novel active contour model (R-DRLSE model) based on level set method is proposed for image segmentation. The R-DRLSE model is a variational level set approach that utilizes the region information to find image contours by minimizing the presented energy functional. To avoid the time-consuming re-initialization step, the distance regularization term is used to penalize the deviation of the level set function from a signed distance function. The numerical implementation scheme of the model can significantly reduce the iteration number and computation time. The results of experiments performed on some synthetic and real images show that the R-DRLSE model is effective and efficient. In particular, our method has been applied to MR kidney image segmentation with desirable results.

A novel active contour model for image segmentation using distance regularization term

Active contour model driven by local histogram fitting energy

Image segmentation is a hot topic of research given its applicability as a pre-processing technique in many image understanding applications. This paper describes the Mumford–Shah variational model for image segmentation. The mathematical framework and the main features of the model are sketched along with the procedure leading from the analytical expression of the model to its practical implementation. The Mumford–Shah functional consists of three weighted terms, the interaction of which assures that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. The solution of the Mumford–Shah variational problem is twofold. On one side, a smooth approximation of the data is built so that the data discontinuities are explicitly preserved from being smoothed. On the other side, the model directly produces an image of the detected discontinuities. An open source software has been developed and used to perform a set of tests on synthetic and real images to demonstrate the feasibility and the effectiveness of the implementation and to give practical evidence of some theoretically foreseen properties of the model. The effect of varying the values of the weight parameters appearing in the Mumford–Shah model has been investigated. In this work, a maximum-likelihood based classifier has been concatenated to the Mumford–Shah model for the processing of a high-resolution orthophoto. The classified image has been compared against the output of the same classifier applied directly to the original orthophoto. Results clearly shows the quality and the practical convenience of variational segmentation. Some promising and interesting extensions of the Mumford–Shah model are also introduced in a dedicated section.

Deshen Xia

Papers

Optical aerial image partitioning using level sets based on modified Chan-Vese model