Medical image segmentation is a fundamental and challenging problem for analyzing medical images. Among different existing medical image segmentation methods, graph-based approaches are relatively new and show good features in clinical applications. In the graph-based method, pixels or regions in the original image are interpreted into nodes in a graph. By considering Markov random field to model the contexture information of the image, the medical image segmentation problem can be transformed into a graph-based energy minimization problem. This problem can be solved by the use of minimum s-t cut/ maximum flow algorithm. This review is devoted to cut-based medical segmentation methods, including graph cuts and graph search for region and surface segmentation. Different varieties of cut-based methods, including graph-cuts-based methods, model integrated graph cuts methods, graph-search-based methods, and graph search/graph cuts based methods, are systematically reviewed. Graph cuts and graph search with deep learning technique are also discussed.

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Arteries in the upper limb play important roles in the circulation system of the human body. In particular, the radial artery has received considerable attention in traditional Chinese medicine for thousands of years. Here, a 3D model for the arm arteries has been created uncomplicated, in a Chinese adult’s left hand, from the magnetic resonance imaging data, using professional modeling software to restore the basic structure of the arm artery in human body, before being imported to Ansys software for simulation. Blood model has been only simulated, and using the blood density of constant parameter and viscosity using the Carreau fluid model, and using viscous-laminar model of Fluent to obtain the velocity profile, static pressure and shear stress in the brachial, interosseous, ulnar, radial and palmar arch arteries. In particular, the brachial and bifurcations have the high pressure and velocity profiles. The simulation results obtained here are also validated by those published in the literature and proved the ulnar artery prevails over the radial artery as a blood supplier to the vessels in the wrist and hand.

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To detect the color face within a complex background, the paper introduced a face detection method using the skin color feature, the hole feature and the template feature. Firstly, the skin color model has been built in the rgb space, and skin color regions are preliminarily segmented according to the skin color feature; then, the hole feature has been used to realize screening of facial regions; finally, facial regions are matched with facial templates to realize face detection. Experimental results using MATLAB show that the method has good detection effect for images that contain one face or more faces. Thus, it has broad application prospects.

Accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is crucial for the analysis of many retinal diseases, such as the screening and diagnosis of glaucoma and atrophy segmentation. Due to domain shift between different datasets caused by different acquisition devices and modes and inadequate training caused by small sample dataset, the existing deep-learning-based OD and OC segmentation networks have poor generalization ability for different fundus image datasets. In this paper, adopting the mixed training strategy based on different datasets for the first time, we propose an encoder-decoder based general OD and OC segmentation network (named as GDCSeg-Net) with the newly designed multi-scale weight-shared attention (MSA) module and densely connected depthwise separable convolution (DSC) module, to effectively overcome these two problems. Experimental results show that our proposed GDCSeg-Net is competitive with other state-of-the-art methods on five different public fundus image datasets, including REFUGE, MESSIDOR, RIM-ONE-R3, Drishti-GS and IDRiD.

Medical image registration can be used for combining information from multiple imaging modalities, monitoring changes in size, shape or image intensity over time intervals. However, the development of such technique can be challenging for 3D spectral-domain optical coherence tomography (SD-OCT) imaging, because SD-OCT image is inherently noisy and its high resolution leads to high complexity of non-rigid registration. In this paper, a new segmentation guided approach is reported for registration of retinal OCT data. The proposed method models the 3D registration as a two-stage registration including x-y direction registration and z direction registration. In x-y direction registration, the vessel maps of OCT projection images between the template and the subject are registered to find out x-y direction displacement. The multi-scale vessel enhancement filter and morphological thinning methods are used to extract the vessel maps from the projection image of 3D OCT scans. And then x-y direction displacement is estimated by matching Speeded-Up Robust Features of the vessel maps. In z direction registration, using the tissue map instead of the original intensity image, A-scans are aligned to get the local displacements in z direction. The proposed method was evaluated on 45 longitudinal retinal OCT scans from 15 subjects. Experimental results show that the proposed method is accurate and very efficient.