Orientador: José Ricardo de Arruda Miranda / Coorientador: Diana Rodrigues de Pina / Banca: Paulo Mazzoncini de Azevedo-Marques / Banca: Trajano Sardenberg / Resumo: A Radiologia Computadorizada (CR - do inglês Computed Radiology) tem sido amplamente utilizada para auxiliar o diagnóstico médico em hospitais e clínicas em todo o mundo. Entretanto a rotina clínica comumente utiliza cartas de técnicas empregadas em sistemas tela-filme na aquisição de imagens em sistemas de detecção CR. As normativas internacionais já estabeleceram parâmetros de testes de controle de qualidade em CR. Entretanto as normativas nacionais trabalham para essa construção. Estudos recentes apontam métodos de otimização de imagens em sistema de detecção CR para exames de crânio, pelve e tórax para pacientes adulto e padrão sem se reportarem a exames de mão. Muitas doenças que acometem as mãos hoje em dia utilizam o exame de raios X como exame auxiliar para diagnóstico. Vale salientar que a técnica utilizada deve fornecer imagens com o máximo de informações possíveis para um diagnóstico seguro, com a menor dose ao paciente, e com redução de custos para a instituição onde os exames são realizados. Neste estudo um fantoma homogêneo de mão foi desenvolvido para ser utilizado no processo de otimização de imagens de mão utilizando sistema CR. Nesse procedimento foram quantificadas as espessuras de distintos tecidos que constituem um fantoma antropomórfico de mão a partir de exames de tomografia computadorizada. O mesmo procedimento foi adotado para 30 exames retrospectivos de pacientes do Hospital das Clínicas da Faculdade de Medicina de Botucatu- UNESP (HCFMB-UNESP). Os resultados obtidos apresentaram concordância entre as espessuras de tecidos que constituem o fantoma antropomórfico e a amostragem de pacientes avaliados, apresentando variações entre 12,63% e 6,48% para tecidos moles e ósseos, respectivamente. O fantoma homogêneo foi utilizado no processo de calibração do feixe de raios X, produzindo técnicas testes. Essas técnicas foram aplicadas em um fantoma antropomórfico de ... / Abstract: Computed Radiology (CR) has been widely used to assist medical diagnosis in hospitals and clinics worldwide. However clinical routine commonly use charts of techniques used in screen-film systems to obtain images in the detection CR systems. The international standards already established parameters of quality control tests in CR. However the national normative work for this construction. Recent studies suggest methods for optimizing images on CR systems for skull, pelvis and thorax exams for adult standard patients without reporting the exams of hand. Many diseases affecting hands today use the X-ray examination as an aid to diagnosis. It is noteworthy that the technique should provide images with maximum possible information for a safety diagnosis, with the lowest dose to the patient, and cost savings to the institution where the exams are performed. In this study a homogeneous phantom hand was developed to be used in the optimization of hand images using CR system. In this procedure were quantified thicknesses of different tissues that constitute an anthropomorphic phantom hand from CT scans. The same procedure was adopted for retrospective examinations of 30 patients of the Hospital das Clinicas, of School Medicine of Botucatu, UNESP (HCFMB-UNESP). The results showed agreement between the thicknesses of tissues that constitute the anthropomorphic phantom and sampling of patients, presenting variations between 12.63% and 6.48% for soft tissue and bone, respectively. The homogeneous phantom was used in the calibration of the X-ray beam process, producing tests technical. These techniques were applied in an anthropomorphic phantom hand from CT scans. The same procedure was adoped for retrospective examinations of 30 patients of the Hospital das Clinicas, of School Medicine of Botucatu, UNESP (HCFMB-UNESP). The results showed agreement between the thicknesses of tissues that constitute the anthropomorphic phantom and sampling of ... / Mestre

No description available.

The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies. / by Deepti Pappusetty. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Indiana University-Purdue University Indianapolis (IUPUI) / A crucial task in computer vision and biomedical image applications is to represent images in a numerically compact form for understanding, evaluating and/or mining their content. The fundamental step of this task is the segmentation of images into regions, given some homogeneity criteria, prior appearance and/or shape information criteria. Specifically, segmentation of cells in microscopic images is the first step in analyzing many biomedical applications. This thesis is a part of the project entitled "Construction and profiling of biodegradable cardiac patches for the co-delivery of bFGF and G-CSF growth factors" funded by National Institutes of Health (NIH). We present a method that simultaneously segments the population of cells while partitioning the cell regions into cytoplasm and nucleus in order to evaluate the spatial coordination on the image plane, density and orientation of cells. Having static microscopic images, with no edge information of a cytoplasm boundary and no time sequence constraints, traditional cell segmentation methods would not perform well. The proposed method combines deformable models with a probabilistic framework in a simple graphical model such that it would capture the shape, structure and appearance of a cell. The process aims at the simultaneous cell partitioning into nucleus and cytoplasm. We considered the relative topology of the two distinct cell compartments to derive a better segmentation and compensate for the lack of edge information. The framework is applied to static fluorescent microscopy, where the cultured cells are stained with calcein AM.

Indiana University-Purdue University Indianapolis (IUPUI) / A novel algorithm was developed to convert raw 2-dimensional endoscope videos into 3-dimensional view. Minimally invasive surgeries aided with 3D view of the invivo site have shown to reduce errors and improve training time compared to those with 2D view. The novelty of this algorithm is that two cues in the images have been used to develop the 3D. Illumination is the rst cue used to nd the darkest regions in the endoscopy images in order to locate the vanishing point(s). The second cue is the presence of ridge-like structures in the in-vivo images of the endoscopy image sequence. Edge detection is used to map these ridge-like structures into concentric ellipses with their common center at the darkest spot. Then, these two observations are used to infer the depth of the endoscopy videos; which then serves to convert them from 2D to 3D. The processing time is between 21 seconds to 20 minutes for each frame, on a 2.27GHz CPU. The time depends on the number of edge pixels present in the edge-detection image. The accuracy of ellipse detection was measured to be 98.98% to 99.99%. The algorithm was tested on 3 truth images with known ellipse parameters and also on real bronchoscopy image sequences from two surgical procedures. Out of 1020 frames tested in total, 688 frames had single vanishing point while 332 frames had two vanishing points. Our algorithm detected the single vanishing point in 653 of the 688 frames and two vanishing points in 322 of the 332 frames.

Indiana University-Purdue University Indianapolis (IUPUI) / Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.