A quantitative evaluation of lung nodule spiculation based on image enhancement

Abstract

Lung cancer manifests itself as lung nodules at an early stage. Segmentation of lung nodules and quantitative evaluation of spiculation can assist physicians in distinguishing benign and malignant lung nodules. The identification of malignant nodules for early diagnosis and treatment can improve the survival rate of patients. In this paper, a quantitative evaluation method of lung nodule spiculation is proposed based on image enhancement. The proposed method combines super-resolution reconstruction-based image enhancement techniques with lung nodule segmentation algorithms to improve the accuracy of segmentation and to quantify the degree of distinctness of the spiculation of lung nodule, which can provide a reliable basis for computer-aided diagnosis and treatment of lung nodules. First, the method uses Laplacian pyramid image restoration technique and Gaussian differential scale-invariant feature to enhance the details and edge information of lung CT images. Then the improved Random Walk algorithm is used to segment the enhanced lung images and extract lung nodules. Finally, the spiculation index, which measures the spiculation of breast nodules, is used to quantify the spiculation of nodules. The experimental results show that the method can effectively segment lung nodules and quantitatively evaluate the spiculation of lung nodules.