Analysis of interframe pulmonary pattern changes for detecting nodules on computed tomography scans

The algorithm for detecting nodules on CT-scans using the analysis of interframe changes of pulmonary pattern has been proposed and implemented as a console application on C++ programming language using the tools of OpenCV. The recognition accuracy has been improved up to 97% when testing the algorithm on the image database provided by the medical establishment

Разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках компьютерного томографа

Цель работы – разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках КТ. В процессе исследования были проанализированы существующие алгоритмы обнаружения образований в легких человека на снимках КТ. В результате исследования был разработан алгоритм для обнаружения образований в легких на снимках КТ, инвариантный к типу образования в легких, а также программные средства, позволившие оценить качество предложенного метода. Область применения: диагностика заболеваний легких человека, сбор статистической информации о видах заболеваний легких. Значимость работы заключается в практической разработке универсального метода для обнаружения образований легких человека – алгоритм показал высокое качество классификации независимо от типа образования.Objective - development of algorithms and program application for lungs nodules detection on CT scans. In the study, common methods and approaches for lungs nodules detection on CT scans have been observed. The results of the study are the developed algorithm for the detection of lungs nodules on CT scans, which is invariant to the type of formations in the lungs, and the implemented software tools that allows evaluating the quality of the proposed method. Scope: diagnostics of human lung diseases, collection of statistical information about the kinds of lung diseases. The significance of the work is the practical development of a universal method for the detection of human lung formations that has shown a high quality of classification, regardless of formation type in the lungs

Разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках компьютерного томографа

Цель работы – разработка алгоритма и программных средств для обнаружения образований в легких человека на снимках КТ. В процессе исследования были проанализированы существующие алгоритмы обнаружения образований в легких человека на снимках КТ. В результате исследования был разработан алгоритм для обнаружения образований в легких на снимках КТ, инвариантный к типу образования в легких, а также программные средства, позволившие оценить качество предложенного метода. Область применения: диагностика заболеваний легких человека, сбор статистической информации о видах заболеваний легких. Значимость работы заключается в практической разработке универсального метода для обнаружения образований легких человека – алгоритм показал высокое качество классификации независимо от типа образования.Objective - development of algorithms and program application for lungs nodules detection on CT scans. In the study, common methods and approaches for lungs nodules detection on CT scans have been observed. The results of the study are the developed algorithm for the detection of lungs nodules on CT scans, which is invariant to the type of formations in the lungs, and the implemented software tools that allows evaluating the quality of the proposed method. Scope: diagnostics of human lung diseases, collection of statistical information about the kinds of lung diseases. The significance of the work is the practical development of a universal method for the detection of human lung formations that has shown a high quality of classification, regardless of formation type in the lungs

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Statistical models in medical image analysis

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 149-156).Computational tools for medical image analysis help clinicians diagnose, treat, monitor changes, and plan and execute procedures more safely and effectively. Two fundamental problems in analyzing medical imagery are registration, which brings two or more datasets into correspondence, and segmentation, which localizes the anatomical structures in an image. The noise and artifacts present in the scans, combined with the complexity and variability of patient anatomy, limit the effectiveness of simple image processing routines. Statistical models provide application-specific context to the problem by incorporating information derived from a training set consisting of instances of the problem along with the solution. In this thesis, we explore the benefits of statistical models for medical image registration and segmentation. We present a technique for computing the rigid registration of pairs of medical images of the same patient. The method models the expected joint intensity distribution of two images when correctly aligned. The registration of a novel set of images is performed by maximizing the log likelihood of the transformation, given the joint intensity model. Results aligning SPGR and dual-echo magnetic resonance scans demonstrate sub-voxel accuracy and large region of convergence. A novel segmentation method is presented that incorporates prior statistical models of intensity, local curvature, and global shape to direct the segmentation toward a likely outcome. Existing segmentation algorithms generally fit into one of the following three categories: boundary localization, voxel classification, and atlas matching, each with different strengths and weaknesses. Our algorithm unifies these approaches. A higher dimensional surface is evolved based on local and global priors such that the zero level set converges on the object boundary. Results segmenting images of the corpus callosum, knee, and spine illustrate the strength and diversity of this approach.by Michael Emmanuel Leventon.Ph.D

Information theoretic regularization in diffuse optical tomography

Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering these parameters of interest involves solving a non-linear and severely ill-posed inverse problem. In this thesis we propose methods towards the regularization of DOT via the introduction of spatially unregistered, a priori information from alternative high resolution anatomical modalities, using the information theory concepts of joint entropy (JE) and mutual information (MI). Such functionals evaluate the similarity between the reconstructed optical image and the prior image, while bypassing the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the modalities involved. By introducing structural a priori information in the image reconstruction process, we aim to improve the spatial resolution and quantitative accuracy of the solution. A further condition for the accurate incorporation of a priori information is the establishment of correct alignment between the prior image and the probed anatomy in a common coordinate system. However, limited information regarding the probed anatomy is known prior to the reconstruction process. In this work we explore the potentiality of spatially registering the prior image simultaneously with the solution of the reconstruction process. We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results obtained by numerical simulations as well as experimental data. In addition we compare the performance of MI and JE. Finally, we propose a method for fast joint entropy evaluation and optimization, which we later employ for the information theoretic regularization of DOT. The main areas involved in this thesis are: inverse problems, image reconstruction & regularization, diffuse optical tomography and medical image registration