Concerning that there is no research about the relationship between disease classification and functional homogeneity analysis of functional protein module in network medicine, the following research work was carried out. Firstly, a gene relationship network was constructed based on the Mesh database and String9 database. Secondly, the gene relationship network was divided by using optimized modularity-based module classification method (such as BGLL, Nonnegtive Matrix Factorization (NMF) and other clustering algorithms). Thirdly, the GO enrichment analysis was carried out for divided modules, and through the comparison of GO enrichment analysis to the high and low pathogenic topology module, important biology suggests for disease classification could be found from protein functional module characteristics in the aspects of biological process, cellular component, molecular function and so on. Finally, the functional characteristics of topological module for disease classification were analyzed, and the data about the functional features of each module was obtained by the analysis to the properties of the network topology such as average degree, density, and average shortest path length, and further correlativity between disease classification and functional module was revealed.

The texture feature extraction algorithm of Pairwise Rotation Invariant Co-occurrence Local Binary Pattern (PRICoLBP) has characteristics of high computing feature dimension, poor rotation invariance and sensitivity to illumination change. In order to solve the issues, an improved PRICoLBP algorithm was proposed. Firstly, the coordinates of two neighboring pixels were obtained by respectively maximizing and minimizing the binary sequence of image pixels. Then, the position coordinates of co-occurred pixel points were calculated via the position coordinates of the center pixel and the two neighboring pixels. Secondly, the texture information of every image pixel was extracted through utilizing the Completed Local Binary Pattern (CLBP) algorithm. Compared with PRICoLBP, the recognition rate of the proposed method was improved respectively by the percentage points of 0.17, 0.24, 2.65, 2.39 and 2.04, on the image libraries of Brodatz, Outex(TC10, TC12), Outex(TC14), CUReT and KTH_TIPS under the same classifier. The experimental results show that the proposed algorithm has better recognition effect for the images with texture rotation variation and illumination change.

Concerning the problem that pedestrians would be partially or seriously shaded by each other in video monitoring, this paper proposed a people counting algorithm based on human body skeleton feature. At first, the initial human skeleton was extracted by morphological skeleton extraction algorithm. Then the optimal skeleton feature was obtained by eliminating outliers and pseudo branches. Finally, this paper established a head detection response rule through analyzing the characteristics of skeleton in head areas to detect the head of pedestrian, and completed people counting by counting the heads of pedestrians. The experimental results show that the algorithm can solve the problems of partial and serious shading in video monitoring. For relatively sparse scene, the overall people counting accuracy rate of the algorithm is about 95%.