Robust active contour segmentation with an efficient global optimizer

Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed, combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information, but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for the proposed active contour method. It is experimentally shown that the proposed method has significant better results in the presence of noise and clutter

Leaf segmentation and tracking using probabilistic parametric active contours

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset

High performance cluster computing with 3-D nonlinear diffusion filters

This paper deals with parallelisation and implementation aspects of PDE-based image processing models for large cluster environments with distributed memory. As an example we focus on nonlinear diffusion filtering which we discretise by means of an additive operator splitting (AOS). We start by decomposing the algorithm into small modules that shall be parallelised separately. For this purpose image partitioning strategies are discussed and their impact on the communication pattern and volume is analysed. Based on the results we develop an algorithmic implementation with excellent scaling properties on massively connected low latency networks. Test runs on a high-end Myrinet cluster yield almost linear speedup factors up to 209 for 256 processors. This results in typical denoising times of 0.5 seconds for five iterations on a 256 x 256 x 128 data cube

An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

Optimal Geodesic Active Contours: Application to Heart Segmentation

We develop a semiautomated segmentation method to assist in the analysis of functional pathologies of the left ventricle of the heart. The segmentation is performed using an optimal geodesic active contour with minimal structural knowledge to choose the most likely surfaces of the myocardium. The use of an optimal segmentation algorithm avoids the problems of contour leakage and false minima associated with variational active contour methods. The resulting surfaces may be analysed to obtain quantitative measures of the heart's function. We have applied the proposed segmentation method to multislice MRI data. The results demonstrate the reliability and efficiency of this scheme as well as its robustness to noise and background clutter

Mathematical modeling for contour identification based on medicinal leaves and GIS images

In this paper, the identification of contour medicinal leaves and GIS images has been determined. The purposes of the Geodesic Active Contour-Additive Operating Splitting (GAC-AOS) modelling are to identify an unknown type of medicinal leaves and edge detection of our images. Besides, three iterative methods such as SOR, RBGS and Jacobi method are used to solve the linear system of equations. In the implementation of the GAC-the AOS model, the experimental result demonstrates that the SOR method gives the best performance compared to the other two methods. The computation platform is based on Intel® CoreTM Duo Processor Architecture with MATLAB version R2011a. The performance analysis is based on the iteration numbers, execution time, accuracy and RMSE