A Method for Lung Boundary Correction Using Split Bregman Method and Geometric Active Contour Model

Abstract

In order to get the extracted lung region from CT images more accurately, a model that contains lung region extraction and edge boundary correction is proposed. Firstly, a new edge detection function is presented with the help of the classic structure tensor theory. Secondly, the initial lung mask is automatically extracted by an improved active contour model which combines the global intensity information, local intensity information, the new edge information, and an adaptive weight. It is worth noting that the objective function of the improved model is converted to a convex model, which makes the proposed model get the global minimum. Then, the central airway was excluded according to the spatial context messages and the position relationship between every segmented region and the rib. Thirdly, a mesh and the fractal theory are used to detect the boundary that surrounds the juxtapleural nodule. Finally, the geometric active contour model is employed to correct the detected boundary and reinclude juxtapleural nodules. We also evaluated the performance of the proposed segmentation and correction model by comparing with their popular counterparts. Efficient computing capability and robustness property prove that our model can correct the lung boundary reliably and reproducibly.

SIFT-GVF-based lung edge correction method for correcting the lung region in CT images

“…A severe problem was mentioned in the original manuscript [ 20 ] when conducting the experiment using the model described. The problem is that the correction result depends on the grid layout significantly.…”

“…Although the precision of segmentation is high, it increases the user burden. Feng et al [ 20 ] divided lung region into several regional blocks using a mesh grid. Then the fractal dimension values were calculated for each block.…”

Active Contours with Automatic qRectify a Deviationq

“…Li C M et al proposed the RSF (Region-Scalable Fitting) model, it can handle images with intensity inhomogeneity [2] ; Lin T Q et al in C-V model, introducing a speed term, quicken the convergence rate of the model [3] . Zhang S H in C-V model to join the regularization term [4] , improved the model, it can handle images with strong noise, its applicable scope expanding [5] ; Yuan Y proposed an adaptive active contours without edges [6] ; Feng C L et al combined with the Split Bregman method and geometric active contour model to deal with medical images [7] ; Zhang Y C et al to improve C-V model, used in multispectral imaging, to enhance the robustness of image segmentation [8] . Based on C-V model, the length term is changed into an automatic "rectify a deviation" term, using the adaptive parameters, got a novel automatic "rectify a deviation" active contours model.…”