Image processing and understanding based on graph similarity testing: algorithm design and software development

Image processing and understanding is a key task in the human visual system. Among all related topics, content based image retrieval and classification is the most typical and important problem. Successful image retrieval/classification models require an effective fundamental step of image representation and feature extraction. While traditional methods are not capable of capturing all structural information on the image, using graph to represent the image is not only biologically plausible but also has certain advantages. Graphs have been widely used in image related applications. Traditional graph-based image analysis models include pixel-based graph-cut techniques for image segmentation, low-level and high-level image feature extraction based on graph statistics and other related approaches which utilize the idea of graph similarity testing. To compare the images through their graph representations, a graph similarity testing algorithm is essential. Most of the existing graph similarity measurement tools are not designed for generic tasks such as image classification and retrieval, and some other models are either not scalable or not always effective. Graph spectral theory is a powerful analytical tool for capturing and representing structural information of the graph, but to use it on image understanding remains a challenge. In this dissertation, we focus on developing fast and effective image analysis models based on the spectral graph theory and other graph related mathematical tools. We first propose a fast graph similarity testing method based on the idea of the heat content and the mathematical theory of diffusion over manifolds. We then demonstrate the ability of our similarity testing model by comparing random graphs and power law graphs. Based on our graph analysis model, we develop a graph-based image representation and understanding framework. We propose the image heat content feature at first and then discuss several approaches to further improve the model. The first component in our improved framework is a novel graph generation model. The proposed model greatly reduces the size of the traditional pixel-based image graph representation and is shown to still be effective in representing an image. Meanwhile, we propose and discuss several low-level and high-level image features based on spectral graph information, including oscillatory image heat content, weighted eigenvalues and weighted heat content spectrum. Experiments show that the proposed models are invariant to non-structural changes on images and perform well in standard image classification benchmarks. Furthermore, our image features are robust to small distortions and changes of viewpoint. The model is also capable of capturing important image structural information on the image and performs well alone or in combination with other traditional techniques. We then introduce two real world software development projects using graph-based image processing techniques in this dissertation. Finally, we discuss the pros, cons and the intuition of our proposed model by demonstrating the properties of the proposed image feature and the correlation between different image features

Study of the Materials Microstructure using Topological Properties of Complex Networks

A method for mapping a two-dimensional color image of the microstructure of the material to a complex network is proposed. Each image elements is assigned to node network. A weighted combination of distance metrics - the Euclidean distance and the Manhattan distance - defines whether there is or not an edge between corresponding nodes. The first metric is used to calculate the spatial distance between the picture elements (pixels), the second metric takes into account the contrast between the brightness of pixels in the gray scale. On the basis of the topological properties of the constructed network the edge pixels were detected that allows us to identify the border areas in the microstructure of materials. The proposed method can be used in automated systems of materialographic analysis

Neutrophil-to-lymphocyte ratio for predicting postoperative recurrence in Crohn’s disease patients with isolated anastomotic lesions

Background: Patients with isolated anastomotic lesions (iAL) are common in postoperative Crohn’s disease (CD) and have heterogeneous prognosis. Objectives: To investigate the prognostic value of neutrophil-to-lymphocyte ratio (NLR) in CD patients with iAL. Design: A bicenter retrospective cohort study. Methods: CD patients who received ileocolonic resection from 2013 and 2020 and had a modified Rutgeerts score of i2a were recruited. NLR was determined within 1 week around the initial endoscopy after ileocolectomy. The primary outcome was clinical recurrence. Kaplan–Meier method and Cox hazard regression analysis were utilized to assess the association between candidate variables and outcomes of interest. Results: In total, 411 postoperative CD patients were preliminarily reviewed and 83 patients were eligible. In total, 36 (48.6%) patients experienced clinical recurrence with a median follow-up time of 16.3 (interquartile range, 9.7–26.3) months. NLR > 2.45 and age at surgery >45 years had higher cumulative incidence of clinical recurrence in the Kaplan–Meier analysis. After adjusted for potential confounders, NLR > 2.45 was the only independent risk factor for clinical recurrence, with an adjusted hazard ratio (HR) of 2.88 [95% confidence interval (CI), 1.39–6.00; p  = 0.005]. Furthermore, a risk score based on NLR and age at surgery were built to further stratify patients. Compared to those who scored 0, patients with a score of 1 and 2 had an adjusted HR of 2.48 (95% CI, 1.22–5.02) and 6.97 (95% CI, 2.19–22.16) for developing clinical recurrence, respectively. Conclusions: NLR is a promising prognostic biomarker for CD patients with iAL. The utilization of NLR and the risk score to stratify patients may facilitate the personalized management in patients with iAL

complete cds

complete cd

Sampling locations, trait data, gene expression data and the raw SNP data

Sampling locations, trait data, gene expression data and the raw SNP dat

sj-tif-4-tag-10.1177_17562848231165129 – Supplemental material for Neutrophil-to-lymphocyte ratio for predicting postoperative recurrence in Crohn’s disease patients with isolated anastomotic lesions

Supplemental material, sj-tif-4-tag-10.1177_17562848231165129 for Neutrophil-to-lymphocyte ratio for predicting postoperative recurrence in Crohn’s disease patients with isolated anastomotic lesions by Rirong Chen, Chao Li, Kang Chao, Yizhe Tie, Jieqi Zheng, Huili Guo, Zhirong Zeng, Li Li, Minhu Chen and Shenghong Zhang in Therapeutic Advances in Gastroenterology</p

sj-tif-2-tag-10.1177_17562848231165129 – Supplemental material for Neutrophil-to-lymphocyte ratio for predicting postoperative recurrence in Crohn’s disease patients with isolated anastomotic lesions

Supplemental material, sj-tif-2-tag-10.1177_17562848231165129 for Neutrophil-to-lymphocyte ratio for predicting postoperative recurrence in Crohn’s disease patients with isolated anastomotic lesions by Rirong Chen, Chao Li, Kang Chao, Yizhe Tie, Jieqi Zheng, Huili Guo, Zhirong Zeng, Li Li, Minhu Chen and Shenghong Zhang in Therapeutic Advances in Gastroenterology</p