The spread of the pulmonary disease among humans is a very rapid process and it stands as the third highest killer in the United States of America. Computed Tomography (CT) scanning allows us to obtain detailed images of the pulmonary anatomy including the airways. The complexity of the tree makes the process of manual segmentation tedious, time-consuming, and variant across individuals. The resultant airway segmentation, whether arrived at manually or through the aid of computers, can then be used to measure airway geometry, study airway reactivity, and guide surgical interventions. The thesis addresses these problems and suggests a fully automated technique for segmenting the airway tree in three-dimensional (3-D) micro-CT images of the thorax of an ex-vivo mouse. This novel technique is a several step approach consisting of: 1. The feature calculation of individual voxels of the micro-CT image, 2. Selection of the best features for classification (obtained from 1), 3. KNN-classification of voxels by the best selected features (from 2) and 4. Region growing segmentation of the KNN classified probability image. KNN classification algorithm has been used for the classification of the voxels of the image (into airway and non-airway voxels) based on the image features, the results of which have then been processed using the region growing segmentation algorithm to obtain the final set of results for segmentation. The segmented airway of the ex-vivo mouse lung volume can be analyzed using a commercial software package to obtain the measurements.

<p>KTH, Division of Neuronic Engineering, have a finite element model of the head. However, this model does not contain detailed modeling of the brain. This thesis project consists of finding a method to extract brain tissues from T1-weighted MR images of the head. The method should be automatic to be suitable for patient individual modeling.</p><p>A summary of the most common segmentation methods is presented and one of the methods is implemented. The implemented method is based on the assumption that the probability density function (pdf) of an MR image can be described by parametric models. The intensity distribution of each tissue class is modeled as a Gaussian distribution. Thus, the total pdf is a sum of Gaussians. However, the voxel values are also influenced by intensity inhomogeneities, which affect the pdf. The implemented method is based on the expectation-maximization algorithm and it corrects for intensity inhomogeneities. The result from the algorithm is a classification of the voxels. The brain is extracted from the classified voxels using morphological operations.</p>

KTH, Division of Neuronic Engineering, have a finite element model of the head. However, this model does not contain detailed modeling of the brain. This thesis project consists of finding a method to extract brain tissues from T1-weighted MR images of the head. The method should be automatic to be suitable for patient individual modeling. A summary of the most common segmentation methods is presented and one of the methods is implemented. The implemented method is based on the assumption that the probability density function (pdf) of an MR image can be described by parametric models. The intensity distribution of each tissue class is modeled as a Gaussian distribution. Thus, the total pdf is a sum of Gaussians. However, the voxel values are also influenced by intensity inhomogeneities, which affect the pdf. The implemented method is based on the expectation-maximization algorithm and it corrects for intensity inhomogeneities. The result from the algorithm is a classification of the voxels. The brain is extracted from the classified voxels using morphological operations.