Study Of Graph Cut/Search Based Medical Image Segmentation Algorithms And Applications

With the rapid development of medical imaging technology,medical imaging has played an increasingly important role in the diagnosis and analysis of clinical diseases,in which the accurate quantitative analysis of tissues,organs and lesion areas provides a strong scientific guarantee for clinical diagnosis and efficacy evaluation.Therefore,clinicians are in urgent need of computer-aided diagnosis and analysis software to achieve accurate segmentation and quantification of tissues,organs and lesion areas,and improve the efficiency and accuracy of clinical diagnosis and analysis.Currently,researchers have proposed a series of segmentation algorithms to address the problems such as grayscale inhomogeneity and noise interference in medical images.However,the multi-modality characteristics of medical images and the fixed topology of target tissues and organs make these image segmentation algorithms still face challenges.On the other hand,graph theory based segmentation algorithms have performed well in recent years.This is because the graph structure in graph theory can effectively establish the relationship between pixels in an image and achieve the segmentation with a fixed topology by special graph construction.Therefore,graph theory based segmentation algorithms are more suitable for the medical image segmentation tasks with multi-modality and fixed topology.In this paper,the principle and implementation of graph cut and graph search algorithms in graph theory are studied in depth,and based on this algorithms,improvements and innovations are proposed for medical image segmentation problems such as multi-modality and fixed topology of segmentation targets.The main research contents and innovations are summarized as follows:1.For the difficulties of lung tumor segmentation based on bi-modality,this paper proposes a bi-modality based segmentation algorithm,which optimizes the graph construction and improves the cost function.This algorithm enlarges the seeds region for graph cut through the random walk algorithm,and proposes random walk energy term and gaussian mixture model energy term,which making full use of the structural and functional information of the two modalities.Finally,a dual sub-graph graph cut model is established to achieve accurate lung tumor segmentation based on the two modalities.In this paper,data from 25 patients with lung tumors were collected for algorithm validation,and promising segmentation results were obtained,validating the feasibility of the algorithm and providing an effective segmentation scheme for multi-modality medical image segmentation tasks.2.For the 3D retinal layer segmentation,this paper proposes an adaptive varying constraints segmentation algorithm,which optimizes the graph construction.This algorithm completes the graph construction based on the shape prior information,and achieves accurate segmentation result.Compared with the traditional graph search algorithm,this algorithm is more flexible and adaptable.It can achieve accurate segmentation result on segmentation tasks with severe morphological differences and sharp changes.In this paper,the algorithm was validated on inner limiting membrane segmentation task with 10 normal eyes and 10 glaucoma eyes,and a mean absolute distance error of 5.38 ?m was obtained,demonstrating the accuracy of the algorithm while providing an improvement and optimization scheme for the wider range of target surface segmentation tasks.3.For the optic disc-cup segmentation in 3D optic nerver head region,this paper proposes a segmentation algorithm based on adaptive varying constraints graph search combined with random forest algorithm.This algorithm overcomes the difficulties of optic disc boundaries detection caused by the factors such as blood vessel shadows using polar coordinate resampling operations,and extractes valid optic disc boundary features using adaptive varying constraints graph search.The algorithm was eventually tested on 30 normal eyes and 35 glaucoma eyes with a Dice coefficient of 0.925,further validating the accuracy of the algorithm,and the algotithm was also developed into software for clinical and scientific use in ophthalmic hospitals4.For the hole structure of retinal layer in optic nerver head region,this paper proposes a 3D retinal layer segmentation algorithm with fixed topological constraints,which optimizes the graph construction.This algorithm makes full use of the shape prior information and improves the accuracy of segmentation.And the topology is qualified by the establishment of shared hole edges,avoiding the problem of segmentation disorder in the retinal layer segmentation task and achieving more accurate segmentation result.In the experimental phase of this paper,this algorithm achieves accurate segmentation of 8 retinal layers on 10 normal eyes and 10 glaucoma eyes with a mean absolute distance error of 7.51 ?m,further validating the accuracy of the algorithm and the feasibility of achieving topological constraints by establishing shared hole edges,and providing a solution for other target segmentation tasks with specific topologies.