Automatic Pathological Lung Segmentation in Low-Dose CT Image Using Eigenspace Sparse Shape Composition.

Abstract

The segmentation of lungs with severe pathology is a nontrivial problem in the clinical application. Due to complex structures, pathological changes, individual differences, and low image quality, accurate lung segmentation in clinical 3-D computed tomography (CT) images is still a challenging task. To overcome these problems, a novel dictionary-based approach is introduced to automatically segment pathological lungs in 3-D low-dose CT images. Sparse shape composition is integrated with the eigenvector space shape prior model, called eigenspace sparse shape composition, to reduce local shape reconstruction error caused by the weak and misleading appearance prior information. To initialize the shape model, a landmark recognition method based on discriminative appearance dictionary is introduced to handle lesions and local details. Furthermore, a new vertex search strategy based on the gradient vector flow field is also proposed to drive the shape deformation to the target boundary. The proposed algorithm is tested on 78 3-D low-dose CT images with lung tumors. Compared to the state-of-the-art methods, the proposed approach can robustly and accurately detect pathological lung surface.