Version du 17 juin 2016 à 14:20

Summary

Female pelvic disorder such as the organ prolapse becomes a very common problem as a woman ages.About 20 to 30% women suffer from the severe degree of prolapse and more than 60% of women who are over 60 years are affected by this pathology.These problems are connected to the mobility of female pelvic system.In this system, a meaningful analysis of medical images usually decides the physicain's diagnosis.However, a human perception or a medical experience cannot be avoided and these two factors may cause the variability in the diagnoses.Hence a semi-automatic method is proposed as an important preliminary step for futher studies and modeling for organ shapes detection.The use of B-splines and offsets has been introduced as an algorithm to create thick surfaces of hollow pelvic organs.This modelling was a step between segmentation and physical modelling.Besides this, a new B-spline-like method is introduced because it is more consistent with the numercial approch.Finally, the model-to-image correlation can be regarded as an energy minimisation problem.To solve this problem,fisrt of all,we should find the spatial correspondance of two medical images, one of which is the virtual image generated from the model.

Main Contribution

The proposed approach can be formulated as an optimisation procedure in the view of computation.Hence, 4 major parts are needed: 1.input data(3D static and 2D dynamic MR images) 2.a mathematical model with variables to be optimised(B-spline Model) 3.a cost function that links the model to the input data(Cost Function Formulation) 4.an optimiser that finds the optimal values of the parameters to minimise the cost function(Optimisation) First of all, we are going to introduce the MRI and Correlation method.This method consists of two types of images,static images and dynamic images.Static images can provide 3D information(sagittal,axial and coronal) while dynamic images are a temporal sequence of 2D images.In the study,the 2D dynamic images are obtained in the same midline sagittal plane of the patient as 3D images.We were mostly interested in segmentations of 3 organs(bladder,vagina and rectum) because both 2D sagittal static and dynamic MR images used these organ segmantations.For the registration procedure of a multi-scale optimisation,we needed two steps:an affine transformation of the model for the coarse registration and a B-spline deformation for the finer registration.To model the geometries of these three organs, the 2D B-spline of 3-degree method is used.In this method, we used a uniformly spaced knot vector to define the basis functions of p-degree.We could also define a span of the B-spline curve by each interval between two degrees.And then each organ can be presented by a parametric B-spline curve and each position on the curve can be calculated.The first and last control point of each curve are attached to form a closed curve.Thus,the model is analytical.As to the affine transformation step,a matrix of mapping was used.To link the model to the input data, we created an appropriate cost function.To calculate,a virtual image is generated from the model that finds the best correlation with the real image.With both the width value and the grey levels, we could detect the real images.As for the registration part,optimisation is the step which can connect the cost function(energy) and the model.

Application