A novel variational model for image registration using Gaussian curvature

Image registration is one important task in many image processing applications. It aims to align two or more images so that useful information can be extracted through comparison, combination or superposition. This is achieved by constructing an optimal trans- formation which ensures that the template image becomes similar to a given reference image. Although many models exist, designing a model capable of modelling large and smooth deformation field continues to pose a challenge. This paper proposes a novel variational model for image registration using the Gaussian curvature as a regulariser. The model is motivated by the surface restoration work in geometric processing [Elsey and Esedoglu, Multiscale Model. Simul., (2009), pp. 1549-1573]. An effective numerical solver is provided for the model using an augmented Lagrangian method. Numerical experiments can show that the new model outperforms three competing models based on, respectively, a linear curvature [Fischer and Modersitzki, J. Math. Imaging Vis., (2003), pp. 81- 85], the mean curvature [Chumchob, Chen and Brito, Multiscale Model. Simul., (2011), pp. 89-128] and the diffeomorphic demon model [Vercauteren at al., NeuroImage, (2009), pp. 61-72] in terms of robustness and accuracy.Comment: 23 pages, 5 figures. Key words: Image registration, Non-parametric image registration, Regularisation, Gaussian curvature, surface mappin

Variational models and numerical algorithms for effective image registration

The goal of image registration is to align two or more images of the same scene obtained at different times, from different perspectives, or sensors such as MRI, X-ray and CT. This step is required to facilitate automatic segmentation for tumour detection or to inform further decisions in treatment planning. It is an important and challenging subject which usually involves high storage, computational cost and dealing with distorted and occluded data. The paradigm behind image registration is to find a reasonable transformation so that the template image becomes similar to the so-called given reference image. Through such transformation, information from these images can be compared or combined. This thesis deals with the mathematical modelling of image registration by way of energy minimisation of a functional. We propose a new decomposition model for image registration which combines parametric transformation and non-parametric deformation. The first category of methods is based on a small number of parameters and for the second category the transformation is based on a functional map (or discretely a large number of parameters) with a regularisation term. We choose one cubic B-spline based model and the linear curvature model for the parametric and non-parametric parts respectively where the overall deformation consists of both global and local displacement for effective image registration. Some results for synthetic and real images will be presented to illustrate the effectiveness of the new model in contrast with the individual models. We then propose a novel variational model for image registration which employs Gaussian curvature as a regulariser. The model is motivated by the surface restoration work in geometric processing [21]. An effective numerical solver is provided for the model using an augmented Lagrangian method. Numerical experiments show that the new model outperforms three competing models based on, respectively, the linear curvature [24], the mean curvature [19] and the diffeomorphic demon models [93] in terms of robustness and accuracy. Finally, we present an improved model for joint segmentation and registration based on active contour without edges. The proposed model is motivated by an earlier model [58] and the linear curvature model [24]. Numerical results show that the new model outperforms the existing model for registration and segmentation of one or multiple objects in the image. The proposed model also leads to improved registration results when features exist inside the object

An improved model for joint segmentation and registration based on linear curvature smoother

Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope. </jats:p

A review on illegals and the stateless in Sabah

Illegal immigrants refer to the migration of people into a country in ways that violates its immigration laws. A stateless person is a person who is not considered as a national by any state under the operation of its law. Interestingly, many who are stateless have never even crossed an international border. The term illegal is very loosely used in places like Sabah where it is interchanged with statelessness. As of 13 November 2018, it was estimated that there are about 12 million stateless people in the world. This review seeks to understand the current demographic situation in Sabah along with the public health repercussions of this demographic change and also to look into the success stories from around the world along with the recommendations from United Nation in solving this matter. Methods: The method used is reviewing of literature of previous studies conducted on statelessness and illegals. Local as well as international studies were reviewed. The data base used was ProQuest. Results: This review identified that one third of Sabah’s population consists of non-locals and in the past 27 years, about half a million illegal immigrants have been deported from Sabah. These numbers pose major public health repercussions from the economic, crime and health point of view. The way forward involves incorporating the United Nation Action plan with the local requirements and settings. Despite challenges, Sabah is trying its best to curb this issue and the Public health repercussions through various initiatives. We have also identified that more public health actions can be taken to reduce the negative effects. Conclusion: Dealing with the stateless and illegals is a delicate matter and there is no one way to solve it. Every country and every state are unique therefore the methods used must be tailor made. This is not something that can be solved within a short period of time and therefore persistence and perseverance is very much needed to tackle this global issue

Medical imaging through histogram equalization and canny edge detection method

Medical images edge detection is an important work for object recognition of the human organs. It is an important pre-processing step in surgery planning to detect the location and size of cancerous tissues or tumor. Today, many edge detection methods were introduced in order to obtain the better result. One of the most popular methods is Canny edge detector. But, Laplacian and Sobel method are still taken into account to the researcher of edge detection. Computer Tomography scan is one of the modalities in medical imaging. Image reconstruction methods were used to obtain the final images in CT scan. Test images from CT scan were used to apply the Canny, Laplacian and Sobel methods using simulation with C++ programming. Comparisons were made and the result show that the combination of Canny edge detector and histogram equalization perform well to the tested images