Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not only for a series of ideal cone shapes, but also for other kinds of 3D shapes as well as other kinds dataset such as MRI. (III). Novel algorithms for recognition of objects based on LiDAR/MRI. Aimed to the sparse LiDAR data, the feature extraction algorithm has been proposed and applied to classify the building and trees. More importantly, the novel algorithms based on level set methods have been provided and employed to recognize not only the buildings and trees, the different trees (e.g. Oak trees and Douglas firs), but also the subthalamus nuclei (STNs). By using the novel algorithms based on level set method, a 3D model of the subthalamus nuclei (STNs) in the brain has been successfully reconstructed based on the statistical data of previous investigations of an anatomy atlas as reference. The 3D rendering of the subthalamic nuclei and the skull directly from MR imaging is also utilized to determine the 3D coordinates of the STNs in the brain. In summary, the novel methods and algorithms of segmentation, recognition and 3D reconstruction of objects have been proposed. The related experiments have been done to test and confirm the validation of the proposed methods. The experimental results also demonstrate the accuracy, efficiency and effectiveness of the proposed methods. A framework for segmentation, recognition and 3D reconstruction of objects has been established, which has been applied to many research areas.

The aim of the first part of this thesis is to explain the theoretical basis of transmission electron microscopy and to mention fundamental parts of transmission electron microscopes. The next part of this work is focused on possible methods of image segmentation, the use of neural networks in the detection of objects in an image and the subsequent clustering of results. The theoretical part of the thesis is concluded with an explanation of some already published methods of automatic detection of biological structures in microscopic images and theoretical design of the algorithm, which will be subsequently developed. The process of training neural networks in order to automatically detect biological structures in an image is described at the beginning of the practical part. This is followed by an evaluation of the results achieved by these networks. Subsequently, cluster analysis methods are applied to these results, the products of which are compared with each other and also with the results obtained by already published methods.

This work deals with a fast level-set approach for segmentation of anatomical structures in volumetric medical images. The fast level-set method evolves a closed 3D surface in time propagating the surface form an initial position. The major contribution of this work is the implementation of the level-set method and construction of an interactive tool for segmentation of 3D medical data using this method. The tool is able to interactively change parameters of the evolution during the segmentation process itself. Due to the nature of level-set method, the evolution process can be stopped at any time, or backtracked and restarted from any previous step with a different configuration.

Background One of the most used treatments for hyperthyroidism, is therapy with radioactive iodine (131I), which is accumulated in the thyroid gland. To determine the activity of 131I to be administered for a certain absorbed dose, the volume of the gland is of great importance but the historically used methods for estimating the functional volume of the gland are based on large approximations. The use of SPECT images enables increased accuracy of functional volume determination. However, there is a need for more realistic phantom studies and improved image segmentation. Aim The aim of this thesis was to find a robust method for image segmentation of the thyroid gland that could adapt to various object sizes and contrasts. The aim was also to develop an accessible and flexible 3D thyroid phantom for measurements and optimisation of parameter settings. Materials and Methods Thyroid phantoms made from playdough loaded with 99mTcO4-, were placed in a neck phantom filled with 99mTcO4- solution of various concentration. SPECT and CT acquisitions of the phantoms were performed and the SPECT images were segmented using thresholding and region growing algorithms. The thresholds in the segmentation algorithms were optimised by minimisation of cost functions consisting of Dice score, against the CT-volume, and relative SPECT volume. To find thresholds that could be used on all phantom volumes and image backgrounds, two overall cost functions were optimised for high and low backgrounds respectively. The optimised thresholds were validated on another set of playdough phantoms. They were also used on a simpler plastic can phantom for comparison of the performance relative to the method used in the clinic today. Results The optimised thresholds showed a substantial divergence between the measurements, ranging from 40 to 58 % for the thresholding algorithm and from 8 to 19 % for the region growing algorithm. The overall optimised thresholds were 55 and 48 % for high and low image backgrounds for the thresholding algorithm which was selected for the validation measurements due to its lower overall cost function and high stability. The developed method indicated a higher accuracy in functional volume determination of the thyroid gland than the standard method used. Conclusions An image segmentation method for functional volume determination of the thyroid gland, that can adapt to image contrast, was developed in this thesis. The method indicates an improved accuracy for functional volume determination of thyroid glands, but more experiments would need to be conducted. The developed thyroid phantoms enable further optimisation of image segmentation parameters for various object sizes, contrasts and shapes. The results indicate that thresholds deduced from simpler phantoms may be too uncertain which might lead to overtreatment of hyperthyroidism with 131I. It was also indicated that thresholding is more suitable than region growing for image segmentation of SPECT images.

By combining computed tomography data with computational fluid dynamics, the cardiac hemodynamics of a patient can be assessed for diagnosis and treatment of cardiac disease. The advantage of computed tomography over other medical imaging modalities is its capability of producing detailed high resolution images containing geometric measurements relevant to the simulation of cardiac blood flow. To extract these geometries from computed tomography data, segmentation of 4D cardiac computed tomography (CT) data has been performed using two deep learning frameworks that combine methods which have previously shown success in other research. The aim of this thesis work was to develop and evaluate a deep learning based technique to segment the left ventricle, ascending aorta, left atrium, left atrial appendage and the proximal pulmonary vein inlets. Two frameworks have been studied where both utilise a 2D multi-axis implementation to segment a single CT volume by examining it in three perpendicular planes, while one of them has also employed a 3D binary model to extract and crop the foreground from surrounding background. Both frameworks determine a segmentation prediction by reconstructing three volumes after 2D segmentation in each plane and combining their probabilities in an ensemble for a 3D output.  The results of both frameworks show similarities in their performance and ability to properly segment 3D CT data. While the framework that examines 2D slices of full size volumes produces an overall higher Dice score, it is less successful than the cropping framework at segmenting the smaller left atrial appendage. Since the full size 2D slices also contain background information in each slice, it is believed that this is the main reason for better segmentation performance. While the cropping framework provides a higher proportion of each foreground label, making it easier for the model to identify smaller structures. Both frameworks show success for use in 3D cardiac CT segmentation, and with further research and tuning of each network, even better results can be achieved.

The thesis deals with the edge detection in the magnetic resonance images and their segmentation. There are adduced the gradient based methods, methods based on zero-crossing in the Laplacian images and also methods combined both of the methods adduced above. These methods are compared to find the best one for the temporo-mandibular joint detection. Consequently sufficient segmentation method for particular parts of the temporo-mandibular joint (the condyle, the acetabulum and the articular disk) separation is chosen.

The subject of this thesis is a method proposed for automated evaluation of the parameters of X-ray of cystic disorders in human jawbones. The main problem in medical diagnostic is the low repeatability due to the subjective evaluation of images without using a tool for image processing. In this thesis are described the basic steps of image processing, various methods of image segmentation and chosen segmentation method live-wire. Selected segments were processed in the ImageJ Java environment. In the cystic regions their basic statistical and shape properties were evaluated. The obtained values were used for learning the classification model (decision tree) in the environment RapidMiner. This model was used to create a plug-in for automatic classification of the type of cysts in the program ImageJ.

The thesis advanced image segmentation for 3D image deals with segmentation and anaglyph 3D views. In the theoretical part of the thesis describes the different approaches were used to image segmentation and closely related methods of image processing. In the following practical part was the implementation of selected methods and created user-friendly applications. The main objective of the program is to identify significant objects in the image. For the purpose of segmentation methods have been implemented based on k-means method, the method of contour and the growth of seeds. The program is created in Visual Studio 2008 and written in C + +. The input and output is the image in various formats (JPG, BMP, TIFF).

This master’s thesis deals with kidney segmentation in perfusion magnetic resonance image sequences. Kidney segmentation is carry out by a few methods such as regionbased techniques, deformable models, specimen-based methods, edge-oriented methods etc. The universal algorithm for patient kidney segmentation still does not exist. Proposed method is an active contour Snake, which is created in programming environment MatLab. Final contours are quantitatively and visually compared to manual kidney segmentation.

Autofluorescence retinal images are obtained with a confocal laser scanning ophthalmoscope, and used for the diagnostic of glaucoma. Glaucoma causes a gradual death of nerve cells and can cause blindness. Retina autofluorescence is caused by pigment lipofuscin, which causes cell damage. The aim of this work was to study methods suitable for segmentation of autofluorescence zones and method for tracking objects in an image. In this project was implemented algorithm of autofluorescence zone detection using method of region growing, designed and realized method for tracking autofluorescence regions.