Measurement of the correlation coefficients between extracted features from CT-scan and MRI images

Background and aims: Nowadays applying computer in image processing is rapidly increasing to resolve shortcomings of medical images. Image features modify its image contained image information. The aim of the present study was to find correlation between CT-scan and MRI images' features. Methods: In this descriptive study, it was used 1458 CT and MRI images of 6 patients (3 females and 3 males) referred to Golestan Hospital in Ahwaz, Iran. After collecting image, pre-processing and feature extract were performed. Then, the images' features were analyzed and correlation coefficients were calculated using Pearson correlation. Results: There was significant relation between most of the extracted features of the CT-scan and the MR (T1-weighted) images (P<0.05). The correlation coefficient between CT-scan images and MR (T1-weighted) images was higher than those of CT-scan images and MRI (T2-weighted). Furthermore, the correlation coefficient between CT-scan images and MRI (T1-weighted) images was higher than those between MR (T1-weighted) and MR (T2-weighted) features' images. Maximum value of the correlation coefficient (0.93) was related to the texture features and its minimum (0.004) was related to the morphological features. Conclusion: The results of this study revealed that there is a significant relationship between extracted features of CT-scan and MRI images, which leads to use a similar algorithm for classification and segmentation studies

Oprogramowanie do rekonstrukcji i analizy 3D obrazów diagnostycznych

Background: Recent advances in computer technologies have opened new frontiers in medical diagnostics. Interesting possibilities are the use of three-dimensional (3D) imaging and the combination of images from different modalities. Software prepared in our laboratories devoted to 3D image reconstruction and analysis from computed tomography and ultrasonography is presented. In developing our software it was assumed that it should be applicable in standard medical practice, i.e. it should work effectively with a PC. An additional feature is the possibility of combining 3D images from different modalities. Materials/Methods: The program was tested on a PC using DICOM data from computed tomography and TIFF files obtained from a 3D ultrasound system. The results of the anthropomorphic phantom and patient data were taken into consideration. A new approach was used to achieve spatial correlation of two independently obtained 3D images. The method relies on the use of four pairs of markers within the regions under consideration. The user selects the markers manually and the computer calculates the transformations necessary for coupling the images. Results: The main software feature is the possibility of 3D image reconstruction from a series of twodimensional (2D) images. The reconstructed 3D image can be: (1) viewed with the most popular methods of 3D image viewing, (2) filtered and processed to improve image quality, (3) analyzed quantitatively (geometrical measurements), and (4) coupled with another, independently acquired 3D image. The reconstructed and processed 3D image can be stored at every stage of image processing. The overall software performance was good considering the relatively low costs of the hardware used and the huge data sets processed. The program can be freely used and tested (source code and program available at http://www.biofizyka.cm-uj.krakow.pl). Improvements allowing the processing of new data types and new procedures can be implemented for specific demands. Conclusions: The reconstruction and data processing can be conducted using a standard PC, so low investment costs result in the introduction of advanced and useful diagnostic possibilities

Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery

This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings

Application of image segmentation and adaptive interpolation techniques to 3D reconstruction of the human temporal bones

xix, 168 leaves : ill. (some col.) ; 29 cm.Includes abstract.Includes bibliographical references (leaves 142-152).Three dimensional models aid otolaryngologists in understanding the complex anatomical features of the human temporal bone. Many of these models are generated by reconstructing histological sections. The goal of this thesis is to provide improvements on these existing 3D reconstruction methods. Presented are a segmentation framework and a contour finding algorithm for histological slices, followed by Gaussian filtering and error analysis. An adaptive interpolation algorithm based on monotonic piecewise cubics is used to automatically generate missing anatomical structure. Part of the algorithm development was completed on CT scans with proposals for extension to histological slices. The contour finding and Gaussian filtering algorithms output valid data points for interpolation. The adaptive interpolation algorithm produces satisfactory results with the interpolation error for the malleus being 1.80% when half of the data is used. The equivalent 3D model volume difference was 0.24%

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods