Image Sequence Stabilization Through Model Based Registration

Acquisition of image series using the digital camera gives a possibility to obtain high resolution/quality animation, much better than while using the digital camcorder. However, there are several problems to deal with when producing animation using such approach. Especially, if motion involves changes in observer position and spatial orientation, the resulting animation may turn out to look choppy and unsmooth. If there is no possibility to provide some hardware based stabilization of the camera during the motion, it is necessary to develop some image processing methods to obtain smooth animation. In this work we deal with the image sequence acquired without stabilization around an object. We propose a method that enables creation of smooth animation using the registration paradigm

Development of the cross-platform framework for the medical image processing

This paper presents the development process of a platform for image processing with a focus on the medical imaging. Besides general image processing algorithms and visualization tools, this platform includes advanced medical imaging modules for segmentation, registration and morphological analysis. It allows fast addition and testing of new algorithms using a modular structure. New modules can be created by using a platform-independent C++ class library and can be easily integrated with a whole system by a plug-in mechanism. An abstract, hierarchical definition language allows the design of efficient graphical user interfaces, hiding the complexity of the underlying module network to the end user

A wide depth of field reconstruction method based on partially focused image series

The main problem with images acquired using macro photography is the very shallow depth-of-field. In this article we present and implement an algorithm to reconstruct full focused images based on partially focused images series of the same object acquired with different focus depths. The presented algorithm consists of several phases including: image registration, depth map creation, image reconstruction and final histogram and quality correction

Computer based framework for cranio-maxillofacial surgery planning

Nowadays the process of surgical planning is a crucial point of every operation in the craniofacial region. In this work we focus on the planning of graft reconstructive surgery for autologous osseous grafts. The planning method consists of two stages. The non-automatic graft design step is followed by a fully automatic procedure to find the best harvesting site in the predefined donor region. The main idea of the proposed method is based on the registration paradigm. The optimal donor site is identified by performing an optimization of the surface based similarity measure between the donor region and the designed graft template. An efficient optimization method based on the Levenberg-Marquardt algorithm has been implemented

Generation of virtual anatomical models from the multimodal medical image data

In this work we present and discuss some methodological issues associated with the generation of the computer based anatomical models. The importance of the virtual patient models is becoming increasingly recognized in modern medicine. The advantages of using such biomedical virtual models are analogous to those of real system behavior simulation in the engineering or material sciences. Particularly significant is its role in the simulation of various pathogenic physiological processes and therapeutical procedures. Such models enable as well the optimization of many diagnostic and therapeutical subroutines. Segmentation, registration, measurement, interaction and visualization modules constitute actually the main pool of the software engineering tools, which enables the surgeon to create a virtual patient-specific anatomical model of the region of interest. Based on this model the physician can simulate and plan different treatment approaches as well as carry out quantitative measurements to identify the optimal therapeutical procedure

Histogram analysis of the human brain MR images based on the S-function membership and Shannon's entropy function

The analysis of medical images for the purpose of computer-aided diagnosis and therapy planning includes segmentation as a preliminary stage for the visualization or quantification. In this paper, we present the first step in our fuzzy segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The histogram analysis based on the S-function membership and the Shannon's entropy function provides finding exact segmentation points. In the final stage, pixel classification is performed using the rule-based fuzzy logic inference. When the segmentation is complete, attributes of these classes may be determined (e.g., volumes), or the classes may be visualized as spatial objects. In contrast to other segmentation methods, like thresholding and region-based algorithms, our methods proceeds automatically and allow more exact delineation of the anatomical structures

Development of heart motion reconstruction framework based on the 4D echocardiographic data

Abnormalities in heart motion can eventually lead to life threatening cardiac injuries therefore measurements of dynamic heart functions are of great clinical importance. The images of moving spatial heart structures can be efficiently acquired using 4D echocardiography. Unfortunately, because of the low quality such images do not allow for precise measurements. To overcome this problem images need to be further processed and moving structures have to be extracted. In this work we present a method for estimating heart motion from the 3D echocardiographic image sequence. On the basis of this method we have developed an application that enables qualitative and quantitative (i.e. volume changes, stroke volume, ejection fraction and cardiac output parameters) description of the heart wall motion. We provide a set of tools for denoising images using the anisotropic diffusion algorithm extended to the fourth dimension and the time averaging method based on non-linear registration efficiently parameterized using the B-spline based Free Form Deformation. We have also developed a non-linear deformable segmentation algorithm for extraction of the inner ventricular surface. The motion of the left ventricle is reconstructed in our approach by recovering deformations of the matter during the cardiac cycle. All the obtained results using our framework can be efficiently presented in 3D using a set of newly developed heart motion visualization tools

Automated geometric features evaluation method for normal foot skeleton model

Normal foot model is a geometric model of a healthy human foot. As the comparison of the processed feet requires a reference ideal healthy foot parameterization it was necessary to create such a model by defining skeleton geometric features and generating the feature set on a dataset population. Manual positioning of such number of landmarks is both a complex and time consuming task for a skilled radiologist, not to mention the total cost of such a procedure. Thus it was recommended to formulate an automated computer algorithm to perform this procedure with accuracy at a comparable level as the manual process. The following paper describes our approach based on automatic landmark positioning in a volumetric foot dataset. The proposed automated procedure is based on four main steps: manual landmark positioning on a reference dataset, registration of the reference dataset with the examined study, transformation of landmark positions from the reference dataset space into the examined dataset space, and calculation of the geometric features on the basis of landmarks positions. The results of our algorithm are presented and discussed in the context of pros and cons of the automated method itself as well as in the context of the generated normal foot model