Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT

Abstract

The first step in lung analysis by CT is the identification of the lung border. To deal with the increased number of sections per scan in thin-slice multidetector CT, it has been crucial to develop accurate and automated lung segmentation algorithms. In this study, an automated method for lung segmentation of thin-slice CT data is presented. The method exploits the advantages of a two-dimensional wavelet edge-highlighting step in lung border delineation. Lung volume segmentation is achieved with three-dimensional (3D) grey level thresholding, using a minimum error technique. 3D thresholding, combined with the wavelet pre-processing step, successfully deals with lung border segmentation challenges, such as anterior or posterior junction lines and juxtapleural nodules. Finally, to deal with mediastinum border under-segmentation, 3D morphological closing with a spherical structural element is applied. The performance of the proposed method is quantitatively assessed on a dataset originating from the Lung Imaging Database Consortium (LIDC) by comparing automatically derived borders with the manually traced ones. Segmentation performance, averaged over left and right lung volumes, for lung volume overlap is 0.983+/-0.008, whereas for shape differentiation in terms of mean distance it is 0.770+/-0.251 mm (root mean square distance is 0.520+/-0.008 mm; maximum distance is 3.327+/-1.637 mm). The effect of the wavelet pre-processing step was assessed by comparing the proposed method with the 3D thresholding technique (applied on original volume data). This yielded statistically significant differences for all segmentation metrics (p<0.01). Results demonstrate an accurate method that could be used as a first step in computer lung analysis by CT.

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

“…These methods can be generally divided into the following four major categories: threshold method [11][12][13][14][15][16][17][18], deformable boundary models [19][20][21][22][23][24], edge-based methods [25][26][27][28], and registration-based method [29,30]. Lungs appear as dark regions in CT scans, since they are essentially bags full of air inside the body.…”

“…Edge points, such as the mediastinal, costal, top, and bottom edge points, were detected using spatial edge-detector filters (SED) and combined to define a closed contour for the lung borders [26]. To highlight lung borders in a stack of 2D images, a 2D wavelet transform is used [27]. An optimal threshold, selected by the minimum error criterion [28], was applied to the wavelet-processed 3D stacks to segment lung volumes.…”

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT

“…The existing techniques for lung segmentation can be classified into three categories, the thresholding-based methods [17][18][19][20][21][22], edgebased methods [23,24], and deformable model based methods [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

“…The lung borders were first detected by 2D wavelet transformation in 2D images. Then, an optimal threshold was implemented to the 3D stacks processed by wavelet to segment lung areas [24].…”

Computational methods for the analysis of functional 4D-CT chest images.

“…In the application to CT chest images, most of the earlier segmentation techniques [32,35,47,52,[65][66][67] presume only the lungs are darker than the other chest tissues, which might result in failure to detect nodules in the case of severe lung pathologies. To avoid such failures, more recent lung segmentation methods, which will be briefly reviewed in this section, consider visual appearances [1,357], shapes [55,[358][359][360][361][362], or hybrid techniques [60,62,[363][364][365] to account for normal and pathological tissues.…”