The aim of this thesis is to investigate the effects of JPEG2000 compression on texture feature extraction from digitized mammograms. A partially automated computer aided diagnosis system is designed, implemented, and tested for this analysis. The system is tested on a database of 60 digital mammograms obtained from the Digital Database for Screening Mammography at the University of South Florida. Using JPEG2000, the mammograms are compressed at 20 different compression ratios ranging from 17:1 to 10,000:1. Two approaches to texture feature extraction are investigated: (i) region of interest (ROI), which is a bounding box around the segmented mass and (ii) rubber band straightening transform (RBST), which is a band of pixels around the segmented mass transformed to a rectangular strip. The gray tone spatial dependent matrices are computed from the ROI and the RBST for the original uncompressed mammograms as well as each group of compressed images. Feature selection and optimization is achieved via stepwise linear discriminant analysis. The efficacy of the features is measured using receiver operator characteristic (ROC) curves. The efficacy of the texture features obtained from the original mammograms is compared to those of the compressed mammograms. Overall, the texture feature efficacy was preserved even for relatively high compression ratios. For example, the area under the ROC curve was greater than 0.99 for compression ratios as high as 5000:1, when the RBST method was utilized. Overall, the JPEG2000 compression distorted the RBST texture features lesser than the ROI texture features.

Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these variability issues in radiomic features due to each CT parameter. To this end, texture phantoms provide a stable geometry and Hounsfield Units (HU) to characterize the radiomic features with respect to image acquisition and reconstruction parameters. In this project, normalization methods were developed to address the variability issues in CT Radiomics using texture phantoms. In the first part of this project, variability in radiomic features due to voxel size variation was addressed. A voxel size resampling method is presented as a preprocessing step for imaging data acquired with variable voxel sizes. After resampling, variability due to variable voxel size in 42 radiomic features was reduced significantly. Voxel size normalization is presented to address the intrinsic dependence of some key radiomic features. After normalization, 10 features became robust as a function of voxel size. Some of these features were identified as predictive biomarkers in diagnostic imaging or useful in response assessment in radiation therapy. However, these key features were found to be intrinsically dependent on voxel size (which also implies dependence on lesion volume). The normalization factors are also developed to address the intrinsic dependence of texture features on the number of gray levels. After normalization, the variability due to gray levels in 17 texture features was reduced significantly. In the second part of the project, voxel size and gray level (GL) normalizations developed based on phantom studies, were tested on the actual lung cancer tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were studied and compared with phantom scans acquired on 8 different CT scanners. Eight out of 10 features showed high (Rs > 0.9) and low (Rs < 0.5) Spearman rank correlations with voxel size before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.9) before and after gray level normalizations, respectively. This work showed that voxel size and GL normalizations derived from texture phantom also apply to lung cancer tumors. This work highlights the importance and utility of investigating the robustness of CT radiomic features using CT texture phantoms. Another contribution of this work is to develop correction factors to address the variability issues in radiomic features due to reconstruction kernels. Reconstruction kernels and tube current contribute to noise texture in CT. Most of texture features were sensitive to correlated noise texture due to reconstruction kernels. In this work, noise power spectra (NPS) was measured on 5 CT scanners using standard ACR phantom to quantify the correlated noise texture. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and the region of interest (ROI) maximum intensity as correction factors. Most texture features were radiation dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percent improvements in robustness of 19 features were in the range of 30% to 78% after corrections. In conclusion, most texture features are sensitive to imaging parameters such as reconstruction kernels, reconstruction Field of View (FOV), and slice thickness. All reconstruction parameters contribute to inherent noise in CT images. The problem can be partly solved by quantifying noise texture in CT radiomics using a texture phantom and an ACR phantom. Texture phantoms should be a pre-requisite to patient studies as they provide stable geometry and HU distribution to characterize the radiomic features and provide ground truths for multi-institutional validation studies.

Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability (<i>GLCP</i>) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify texture boundaries. Proposed is an advancement to the <i>GLCP</i> method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as <i>WGLCP</i> (weighted <i>GLCP</i>) texture features. In this research, the <i>WGLCP</i> and <i>GLCP</i> feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The <i>WGLCP</i> method outperforms the <i>GLCP</i> method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the <i>GLCP</i> correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the <i>GLCP</i> correlation statistical feature decreases segmentation accuracy. When comparing <i>WGLCP</i> and <i>GLCP</i> features for segmentation, the <i>WGLCP</i> features provide higher segmentation accuracy.

Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability (<i>GLCP</i>) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify texture boundaries. Proposed is an advancement to the <i>GLCP</i> method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as <i>WGLCP</i> (weighted <i>GLCP</i>) texture features. In this research, the <i>WGLCP</i> and <i>GLCP</i> feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The <i>WGLCP</i> method outperforms the <i>GLCP</i> method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the <i>GLCP</i> correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the <i>GLCP</i> correlation statistical feature decreases segmentation accuracy. When comparing <i>WGLCP</i> and <i>GLCP</i> features for segmentation, the <i>WGLCP</i> features provide higher segmentation accuracy.

Title from PDF of title page. / Document formatted into pages; contains 63 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: The work presented here is an important component of an on going project of developing an automated mass classification system for breast cancer screening and diagnosis for Digital Mammogram applications. Specifically, in this work the task of automatically separating mass tissue from normal breast tissue given a region of interest in a digitized mammogram is investigated. This is the crucial stage in developing a robust automated classification system because the classification depends on the accurate assessment of the tumor-normal tissue border as well as information gathered from the tumor area. In this work the Expectation Maximization (EM) method is developed and applied to high resolution digitized screen-film mammograms with the aim of segmenting normal tissue from mass tissue. / ABSTRACT: Both the raw data and summary data generated by Laws' texture analysis are investigated. Since the ultimate goal is robust classification, the merits of the tissue segmentation are assessed by its impact on the overall classification performance. Based on the 300 image dataset consisting of 97 malignant and 203 benign cases, a 63% sensitivity and 89% specificity was achieved. Although, the segmentation requires further investigation, the development and related computer coding of the EM algorithm was successful. The method was developed to take in account the input feature correlation. This development allows other researchers at this facility to investigate various input features without having the intricate understanding of the EM approach. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

The work presented here is an important component of an on going project of developing an automated mass classification system for breast cancer screening and diagnosis for Digital Mammogram applications. Specifically, in this work the task of automatically separating mass tissue from normal breast tissue given a region of interest in a digitized mammogram is investigated. This is the crucial stage in developing a robust automated classification system because the classification depends on the accurate assessment of the tumor-normal tissue border as well as information gathered from the tumor area. In this work the Expectation Maximization (EM) method is developed and applied to high resolution digitized screen-film mammograms with the aim of segmenting normal tissue from mass tissue. Both the raw data and summary data generated by Laws' texture analysis are investigated. Since the ultimate goal is robust classification, the merits of the tissue segmentation are assessed by its impact on the overall classification performance. Based on the 300 image dataset consisting of 97 malignant and 203 benign cases, a 63% sensitivity and 89% specificity was achieved. Although, the segmentation requires further investigation, the development and related computer coding of the EM algorithm was successful. The method was developed to take in account the input feature correlation. This development allows other researchers at this facility to investigate various input features without having the intricate understanding of the EM approach.

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Este trabalho apresenta o estudo e implementação de um banco de conhecimentos para auxiliar o diagnóstico de lesões da mama por inspeção visual, permitindo ao médico consultas através de características pictóricas da imagem e a comparação visual entre imagem investigada e imagens previamente classificadas e suas informações clínicas. As imagens encontram-se classificadas no banco de conhecimentos segundo o padrão \"Breast imaging reporting and data systems\" (BI-RADS) do Colégio Americano de Radiologia. A seleção das imagens, informações clínicas representativas, bem como sua classificação foram realizada em conjunto com médicos radiologistas do Centro de Ciências das Imagens e Física Médica (CCIFM) da Faculdade de Medicina de Ribeirão Preto (FMRP) da Universidade de São Paulo (USP). O processo de indexação e recuperação das imagens é baseado em atributos de textura extraídos de \"Regions of interest\" (ROIs) previamente estabelecidas em mamogramas digitalizados. Para simplificar este processo, foi utilizado a Análise de Componentes Principais (PCA), que visa a redução do número de atributos de textura e as informações redundantes existentes. Os melhores resultados obtidos foram para as ROIs 139 (Precisão = 0.80), 59 (Precisão = 0.86) e um valor de 100% de acerto para a ROI 40. / This work presents the survey and implementation of a database of knowledge to aid the diagnostic of breast lesions through visual inspection, allowing the physician a seach through the characteristics of the contents of the image and the visual comparison between the analysed image and the previously classified images and its clinical information. The images are classified into the database of knowledge according to the pattern Breast Imaging Reporting and Data Systems (BI-RADS) of the American College of Radiology. The selection of the images, the representative clinical information, as well as its classification have been performed in conjunction with practictioners radiologists of the Centro de Ciências das Imagens e Física Médica (CCIFM) from Faculdade de Medicina de Ribeirão Preto (FMRP) from Universidade de São Paulo (USP). The process of indexing and retrieving the images is based on characteristic of the texture extracted from the regions of interest (ROIs) previously established through scanned mammograms. To simplify this path, the Principal Components Analysis (PCA) was used it aims the reduction of the number of features of texture and the existing redundant information. The best results obtained were to the ROIs 139 (precision = 0.80), 59 (precision = 0.86) and a value of 100% of precision for ROI 40.

Este trabalho apresenta o estudo e implementação de um banco de conhecimentos para auxiliar o diagnóstico de lesões da mama por inspeção visual, permitindo ao médico consultas através de características pictóricas da imagem e a comparação visual entre imagem investigada e imagens previamente classificadas e suas informações clínicas. As imagens encontram-se classificadas no banco de conhecimentos segundo o padrão \"Breast imaging reporting and data systems\" (BI-RADS) do Colégio Americano de Radiologia. A seleção das imagens, informações clínicas representativas, bem como sua classificação foram realizada em conjunto com médicos radiologistas do Centro de Ciências das Imagens e Física Médica (CCIFM) da Faculdade de Medicina de Ribeirão Preto (FMRP) da Universidade de São Paulo (USP). O processo de indexação e recuperação das imagens é baseado em atributos de textura extraídos de \"Regions of interest\" (ROIs) previamente estabelecidas em mamogramas digitalizados. Para simplificar este processo, foi utilizado a Análise de Componentes Principais (PCA), que visa a redução do número de atributos de textura e as informações redundantes existentes. Os melhores resultados obtidos foram para as ROIs 139 (Precisão = 0.80), 59 (Precisão = 0.86) e um valor de 100% de acerto para a ROI 40. / This work presents the survey and implementation of a database of knowledge to aid the diagnostic of breast lesions through visual inspection, allowing the physician a seach through the characteristics of the contents of the image and the visual comparison between the analysed image and the previously classified images and its clinical information. The images are classified into the database of knowledge according to the pattern Breast Imaging Reporting and Data Systems (BI-RADS) of the American College of Radiology. The selection of the images, the representative clinical information, as well as its classification have been performed in conjunction with practictioners radiologists of the Centro de Ciências das Imagens e Física Médica (CCIFM) from Faculdade de Medicina de Ribeirão Preto (FMRP) from Universidade de São Paulo (USP). The process of indexing and retrieving the images is based on characteristic of the texture extracted from the regions of interest (ROIs) previously established through scanned mammograms. To simplify this path, the Principal Components Analysis (PCA) was used it aims the reduction of the number of features of texture and the existing redundant information. The best results obtained were to the ROIs 139 (precision = 0.80), 59 (precision = 0.86) and a value of 100% of precision for ROI 40.

The document is discussing the issue of the computer vision with ability to character recignition in the image. Wavelet transform is used for preprocessing the image. Pixel energy feature is firstly used for searchich candidate text pixels. Density region growing method is then used to collect candidate pixels to the separate regions, which will be candidate text regions. Several of the features are calculated over the regions and the SVM classifier is used to derive, if the region is really a text region or not.