Traditional robust principal component analysis (RPCA) is very prone to voids in the process of background/foreground separation of complex scene videos and easy to misjudge the dynamic background as a moving target, which makes the separation effect unideal. In order to address this problem, this paper introduces the super-pixel segmentation technique into the RPCA model. First, the Linear Spectral Clustering algorithm (LSC) is used to mark the super-pixel segmentation of the video sequence and a superpixel grouping matrix is obtained. Then a new video background/foreground separation model is proposed based on the non-convex rank approximation RPCA and super-pixel motion detection (SPMD) technique. The Otsu algorithm is used to obtain the motion mask matrix and the augmented lagrange alternating direction method is used to solve the improved RPCA model. The results of numerical experiment show that the method proposed in this paper has a higher accuracy in the detection of moving objects in dynamic background. INDEX TERMS Video background/foreground separation, RPCA, superpixel segmentation, linear spectral clustering algorithm, Otsu algorithm, motion mask.

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.

The changes of retinal vessel width are the key steps in the accurate diagnosis, early treatment and surgical planning of ocular diseases. The segmentation of retinal vessel is the basis of computer-aided diagnosis and large-scale disease screening. In this paper, we propose an algorithm for segmentation of fundus image based on image enhancement. At first, the ocular fundus image is enhanced by Laplacian pyramid, and the enhanced image vessel is more obvious than the background. Then, an improved matching filter algorithm is used to segment the ocular fundus image vessel. Experimental results show that the proposed method is more accurate than the traditional segmentation method. And it has strong robustness to the fundus images under different illumination conditions.