Active Contour Models For Object Extraction In Medical Images

Medical image segmentation is a precondition of aided diagnosis, quantitative analysis and surgical planning in clinical application. Due to the wide variety of shapes, the complexity of the topology, the presence of noise in a complicated background, and the diversity of imaging techniques, it is a very difficult task to extract medical object automatically and accurately. In order to overcome these difficulties, there are two topics could be done. One is to choose an optimized model. As an efficient segmentation tool in medical image, active contour model has made a great success in medical application. The other is to make full use of object's prior knowledge and embed them into an active contour model. In this paper, several specific active contour models are proposed according to specific clinical application, which are Region Competition Based Active Contour Model, Vascular Active Contour Model, Multi Active Contour Models for liver modeling, and Region and Shape prior base Geodesic Active Contour Models. All of them are results of combination of certain prior knowledge and active contour. On the base of these segmentation models, some quantitative analysis is made on certain organs and diseases.Firstly, we review the recent advances in prior-based image segmentation and analyze them according to a general framework which consists of three parts: original image features, segmentation models and prior knowledge. The presentation of prior knowledge and how to embed it in a segmentation model are emphasized. Then, as the most important segmentation model, active contour models are reviewed in detail, which include Snake and level set model. At last, some points, such as characteristic of prior knowledge, challenges of active contour and promising research directions are presented. Secondly, a probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour. The algorithms are derived by minimizing a region based probabilistic energy function and implemented in a level set framework. An additional speed-controlling term makes the active contour quickly convergent to the actual contour on strong edges, whereas a probabilistic model makes the active contour performing well for weak edges. Prior knowledge about the initial contour and the probabilistic distribution contributes to more efficient extraction. The developed model has been applied to a variety of medical images, from CTA and MRA of the coronary to rotationally scanned and real-time three-dimensional echocardiography images of the mitral valve. As the results show, the algorithm is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.Thirdly, a novel active contour model is proposed for vessel tree segmentation which makes full use of all available vascular information. Firstly, we introduce a region competition based active contour exploiting prior intensity distribution information to segment thick vessels robustly and accurately. Secondly, we define a vector field, resulting from the eigen analysis of the Hessian matrix of image intensity and used by the active contour to evolve into the thin vessels. The vector field is also specified in a multi-scale framework. Finally, a vascular smoothened term takes a strategy combined with minimal principal curvature and mean curvature, which make it smoothes surface without changing the shape of the vessel tree. The developed model has been applied to liver vessel tree, coronary artery and lung vessel extraction. Some comparisons are made between Geodesic Active Contour, CURVES, C-V and our model. The experiments show that the model is fast, accurate, robust and suited for an automatic procedure in vessel tree extraction.Fourthly, in order to extract the liver, its tumors and vessels, we developed an active contour model with an embedded classifier, based on a Gaussian mixture model fitted to the intensity distribution of the medical image. The difference between the maximum membership of the intensities belonging to the classes of the object and those of the background, is included as an extra speed propagation term in the active contour model. An additional speed controlling term slows down the evolution of the active contour when it approaches an edge, making it quickly convergent to the ideal object. The developed model has been applied to liver segmentation. Some comparisons are made between Geodesic Active Contour, C-V and our model. As the experiments show, our model is accurate, flexible and suited to extract objects surrounded by a complicated background.At last, we present an automated 3D echocardiography image protocol for quantitative analysis of mitral valve. A region and shape prior of the cardiac valve is presented in form of speed field and incorporate it into image segmentation within level set framework. Region prior constrains the zero level set evolving in certain region and shape prior pulls the curve to the ideal contour. On the base of automatic mitral detection method, we quantitate some important geometrical parameters of mitral valve in an automatic way, which include metal valve orifice area, annular area, LV volume, and leaflet opening angle. Some comparisons with Snake method and manual parameterization results show the validity of our methods.