<p>Tissue stiffness can be an indicator of various types of ailments. However, no standard diagnostic imaging modality has the capability to depict the stiffness of tissue. To overcome this deficiency, various elasticity imaging methods have been proposed over the past 20 years. A promising technique for elasticity imaging is acoustic radiation force impulse (ARFI) based shear wave imaging. Spatially localized acoustic radiation force excitation is applied impulsively to generate shear waves in tissue and its stiffness is quantified by measuring the shear wave speed (SWS).</p><p>The aim of this thesis is to contribute to both the clinical application of ARFI shear wave imaging and its technical development using the latest advancements in ultrasound imaging capabilities.</p><p>To achieve the first of these two goals, a pilot imaging study was conducted to evaluate the suitability of ARFI shear wave imaging for the assessment of liver fibrosis using a rodent model of the disease. The stiffness of severely fibrotic rat livers were found to be significantly higher than healthy livers. In addition, liver stiffness was correlated with fibrosis as quantified using collagen content.</p><p>Based on these findings, an imaging study was conducted on patients undergoing liver biopsy at the Duke University Medical Center. A robust SWS estimation algorithm was implemented to deal with noisy patient shear wave data using the random sample consensus (RANSAC) approach. RANSAC estimated liver stiffness was found to be higher in severely fibrotic and cirrhotic livers, suggesting that ARFI shear wave imaging may potentially be useful for the staging of severe</p><p>fibrosis in humans.</p><p>To achieve the second aim of this thesis, a system capable of monitoring ARFI induced shear wave propagation in 3D was implemented using a 2D matrix array transducer. This capability was previously unavailable with conventional 1D arrays. This system was used to study the precision of time-of-flight (TOF) based SWS estimation. It was found that by placing tracking beam locations at the edges of the SWS measurement region of interest using the 2D matrix array, TOF SWS precision could be improved in a homogeneous medium.</p><p>The 3D shear wave imaging system was also used to measure the SWS in muscle, which does not conform to the isotropic mechanical behavior usually assumed for tissue, due to the parallel arrangement of muscle fibers. It is shown that the SWS along and across the fibers, as well as the 3D fiber orientation can be estimated from a single 3D shear wave data-set. In addition, these measurements can be made independent of the probe orientation relative to the fibers. This suggests that 3D shear wave imaging can be useful for characterizing anisotropic mechanical properties of tissue.</p> / Dissertation

<p>The rupture of arterial plaques is the most common cause of ischemic complications including stroke, the fourth leading cause of death and number one cause of long term disability in the United States. Unfortunately, because conventional diagnostic tools fail to identify plaques that confer the highest risk, often a disabling stroke and/or sudden death is the first sign of disease. A diagnostic method capable of characterizing plaque vulnerability would likely enhance the predictive ability and ultimately the treatment of stroke before the onset of clinical events.</p><p>This dissertation evaluates the hypothesis that Acoustic Radiation Force Impulse (ARFI) imaging can noninvasively identify lipid regions, that have been shown to increase a plaque's propensity to rupture, within carotid artery plaques <italic>in vivo</italic>. The work detailed herein describes development efforts and results from simulations and experiments that were performed to evaluate this hypothesis.</p><p>To first demonstrate feasibility and evaluate potential safety concerns, finite-element method simulations are used to model the response of carotid artery plaques to an acoustic radiation force excitation. Lipid pool visualization is shown to vary as a function of lipid pool geometry and stiffness. A comparison of the resulting Von Mises stresses indicates that stresses induced by an ARFI excitation are three orders of magnitude lower than those induced by blood pressure. This thesis also presents the development of a novel pulse inversion harmonic tracking method to reduce clutter-imposed errors in ultrasound-based tissue displacement estimates. This method is validated in phantoms and was found to reduce bias and jitter displacement errors for a marked improvement in image quality <italic>in vivo</italic>. Lastly, this dissertation presents results from a preliminary <italic>in vivo</italic> study that compares ARFI imaging derived plaque stiffness with spatially registered composition determined by a Magnetic Resonance Imaging (MRI) gold standard in human carotid artery plaques. It is shown in this capstone experiment that lipid filled regions in MRI correspond to areas of increased displacement in ARFI imaging while calcium and loose matrix components in MRI correspond to uniformly low displacements in ARFI imaging.</p><p>This dissertation provides evidence to support that ARFI imaging may provide important prognostic and diagnostic information regarding stroke risk via measurements of plaque stiffness. More generally, the results have important implications for all acoustic radiation force based imaging methods used clinically.</p> / Dissertation

<p>In 2011, an estimated 230,480 new cases of invasive breast cancer were diagnosed among women, as well as an estimated 57,650 additional cases of in situ breast cancer [1]. Breast conserving surgery (BCS) is a recommended surgical choice for women with early stage breast cancer (stages 0, I, II) and for those with Stage II-III disease who undergo successful neo-adjuvant treatment to reduce their tumor burden [2, 3]. Cancer within 2mm of a margin following BCS increases the risk of local recurrence and mortality [4-6]. Margin assessment presents an unmet clinical need. Breast tissue is markedly heterogeneous which makes identifying cancer foci within benign tissue challenging. Optical spectroscopy can provide surgeons with intra-operative diagnostic tools. Here, ex-vivo breast tissue is evaluated to determine which sources of optical contrast have the potential to detect malignancy at the margins in women of differing breast composition. Then, H&E images of ex-vivo breast tissue sites are quantified to further deconstruct the relationship between optical scattering and the underlying tissue morphology. </p><p>Diffuse reflectance spectra were measured from benign and malignant sites from the margins of lumpectomy specimens. Benign and malignant sites were compared and then stratified by tissue type and depth. The median and median absolute deviance (MAD) was calculated for each category. The frequencies of the benign tissue types were separated by menopausal status and compared to the corresponding optical properties. </p><p>H&E images were then taken of the malignant and benign sites and quantified to describe the % adipose, % collagen and % glands. Adipose sites, images at 10x, were predominantly fatty and quantified according to adipocyte morphology. H&E-stained adipose tissue sections were analyzed with an automated image processing algorithm to extract average cell area and cell density. Non-adipose sites were imaged with a 2.5x objective. Grids of 200µm boxes corresponding to the 3mm x 2mm area were overlaid on each non-adipose image. The non-adipose images were classified as the following: adipose and collagen (fibroadipose); collagen and glands (fibroglandular); adipose, collagen and glands (mixed); and malignant sites. Correlations between <&mus&#8242;> and % collagen in were determined in benign sites. Age, BMI, and MBD were then correlated to <&mus&#8242;> in the adipose and non-adipose sites. Variability in <&mus&#8242;> was determined to be related to collagen and not adipose content. In order to further investigate this relationship, the importance of age, BMI and MBD was analyzed after adjusting for the % collagen. Lastly, the relationship between % collagen and % glands was analyzed to determine the relative contributions of % collagen and % glands <&mus&#8242;>. Statistics were calculated using Wilcoxon rank-sum tests, Pearson correlation coefficients and linear fits in R. </p><p> The diagnostic ability of the optical parameters was linked to the distance of tumor from the margin as well as menopausal status. [THb] showed statistical differences from <&mus&#8242;> between malignant (<&mus&#8242;>: 8.96cm-1±2.24MAD, [THb]: 42.70&muM±29.31MAD) compared to benign sites (<&mus&#8242;>: 7.29cm-1±2.15MAD, [THb]: 32.09&muM±16.73MAD) (p<0.05). Fibroglandular (FG) sites exhibited increased <&mus&#8242;> while adipose sites showed increased [&beta-carotene] within benign tissues. Scattering differentiated between ductal carcinoma in situ (DCIS) (9.46cm-1±1.06MAD) and invasive ductal carcinoma (IDC) (8.00cm-1±1.81MAD), versus adipose sites (6.50cm-1±1.95MAD). [&beta-carotene] showed marginal differences between DCIS (19.00&muM±6.93MAD, and FG (15.30&muM±5.64MAD). [THb] exhibited statistical differences between positive sites (92.57&muM±18.46MAD) and FG (34.12&muM±22.77MAD), FA (28.63&muM±14.19MAD), and A (30.36&muM±14.86MAD). Due to decreased fibrous content and increased adipose content, benign sites in post-menopausal patients exhibited lower <&mus&#8242;>, but higher [&beta-carotene] than pre-menopausal patients.</p><p>Further deconstructing the relationship between optical scattering and tissue morphology resulted in a positive relationship between <&mus&#8242;> and % collagen (r=0.28, p=0.00034). Increased variability was observed in sites with a higher percentage of collagen. In adipose tissues MBD was negatively correlated with age (r=-0.19, p=0.006), BMI (r=-0.33, p=2.3e-6) and average cell area (r=-0.15, p=0.032) but positively related to the log of the average cell density (r=0.17, p=0.12). In addition, BMI was positively correlated to average cell area (r=0.31, p=1.2e-5) and negatively related to log of the cell density (r=-0.28, p=7.6e-5). In non-adipose sites, age was negatively correlated to <&mus&#8242;> in benign (r=-0.32, p=4.7e-5) and malignant (r=-0.32, p=1.4e-5) sites and this correlation varied significantly by the collagen level (r=-0.40 vs. -0.13). BMI was negatively correlated to <&mus&#8242;> in benign (r=-0.32, p=4e-5) and malignant (r=-0.31, p=2.8e-5) sites but this relationship did not vary by collagen level. MBD was positively correlated to <&mus&#8242;> in benign (r=0.22, p=0.01) and malignant (r=0.21, p=4.6e-3) sites. Optical scattering was shown to be tied to patient demographics. Lastly, the analysis of collagen vs. glands was narrowed to investigate sites with glands between 0-40% (the dynamic range of the data), the linear model reflected an equivalent relationship to scattering from % glands and the % collagen in benign sites (r=0.18 vs. r=0.17). In addition, the malignant sites showed a stronger positive relationship (r=0.64, p=0.005) to <&mus&#8242;> compared to the benign sites (r=0.52, p=0.03).</p><p>The data indicate that the ability of an optical parameter to differentiate benign from malignant breast tissues is dictated by patient demographics. Scattering differentiated between malignant and adipose sites and would be most effective in post-menopausal women. [&beta-carotene] or [THb] may be more applicable in pre-menopausal women to differentiate malignant from fibrous sites. Patient demographics are therefore an important component to incorporate into optical characterization of breast specimens. Through the subsequent stepwise analysis of tissue morphology, <&mus&#8242;> was positively correlated to collagen and negatively correlated to age and BMI. Increased variability of <&mus&#8242;> with collagen level was not dependent on the adipose contribution. A stronger correlation between age and <&mus&#8242;> was seen in high collagen sites compared to low collagen sites. Contributions from collagen and glands to <&mus&#8242;> were independent and equivalent in benign sites; glands showed a stronger correlation to <&mus&#8242;> in malignant sites than collagen. This information will help develop improved scattering models and additional technologies from separating fibroglandular sites from malignant sites and ultimately improve margin assessment.</p> / Dissertation

abstract: Three dimensional (3-D) ultrasound is safe, inexpensive, and has been shown to drastically improve system ease-of-use, diagnostic efficiency, and patient throughput. However, its high computational complexity and resulting high power consumption has precluded its use in hand-held applications. In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes. Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology. Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology. Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

abstract: Recent advances in medical imaging technology have greatly enhanced imaging based diagnosis which requires computational effective and accurate algorithms to process the images (e.g., measure the objects) for quantitative assessment. In this dissertation, one type of imaging objects is of interest: small blobs. Example small blob objects are cells in histopathology images, small breast lesions in ultrasound images, glomeruli in kidney MR images etc. This problem is particularly challenging because the small blobs often have inhomogeneous intensity distribution and indistinct boundary against the background. This research develops a generalized four-phased system for small blob detections. The system includes (1) raw image transformation, (2) Hessian pre-segmentation, (3) feature extraction and (4) unsupervised clustering for post-pruning. First, detecting blobs from 2D images is studied where a Hessian-based Laplacian of Gaussian (HLoG) detector is proposed. Using the scale space theory as foundation, the image is smoothed via LoG. Hessian analysis is then launched to identify the single optimal scale based on which a pre-segmentation is conducted. Novel Regional features are extracted from pre-segmented blob candidates and fed to Variational Bayesian Gaussian Mixture Models (VBGMM) for post pruning. Sixteen cell histology images and two hundred cell fluorescent images are tested to demonstrate the performances of HLoG. Next, as an extension, Hessian-based Difference of Gaussians (HDoG) is proposed which is capable to identify the small blobs from 3D images. Specifically, kidney glomeruli segmentation from 3D MRI (6 rats, 3 humans) is investigated. The experimental results show that HDoG has the potential to automatically detect glomeruli, enabling new measurements of renal microstructures and pathology in preclinical and clinical studies. Realizing the computation time is a key factor impacting the clinical adoption, the last phase of this research is to investigate the data reduction technique for VBGMM in HDoG to handle large-scale datasets. A new coreset algorithm is developed for variational Bayesian mixture models. Using the same MRI dataset, it is observed that the four-phased system with coreset-VBGMM has similar performance as using the full dataset but about 20 times faster. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2015

abstract: Colorectal cancer is the second-highest cause of cancer-related deaths in the United States with approximately 50,000 estimated deaths in 2015. The advanced stages of colorectal cancer has a poor five-year survival rate of 10%, whereas the diagnosis in early stages of development has showed a more favorable five-year survival rate of 90%. Early diagnosis of colorectal cancer is achievable if colorectal polyps, a possible precursor to cancer, are detected and removed before developing into malignancy. The preferred method for polyp detection and removal is optical colonoscopy. A colonoscopic procedure consists of two phases: (1) insertion phase during which a flexible endoscope (a flexible tube with a tiny video camera at the tip) is advanced via the anus and then gradually to the end of the colon--called the cecum, and (2) withdrawal phase during which the endoscope is gradually withdrawn while colonoscopists examine the colon wall to find and remove polyps. Colonoscopy is an effective procedure and has led to a significant decline in the incidence and mortality of colon cancer. However, despite many screening and therapeutic advantages, 1 out of every 4 polyps and 1 out of 13 colon cancers are missed during colonoscopy. There are many factors that contribute to missed polyps and cancers including poor colon preparation, inadequate navigational skills, and fatigue. Poor colon preparation results in a substantial portion of colon covered with fecal content, hindering a careful examination of the colon. Inadequate navigational skills can prevent a colonoscopist from examining hard-to-reach regions of the colon that may contain a polyp. Fatigue can manifest itself in the performance of a colonoscopist by decreasing diligence and vigilance during procedures. Lack of vigilance may prevent a colonoscopist from detecting the polyps that briefly appear in the colonoscopy videos. Lack of diligence may result in hasty examination of the colon that is likely to miss polyps and lesions. To reduce polyp and cancer miss rates, this research presents a quality assurance system with 3 components. The first component is an automatic polyp detection system that highlights the regions with suspected polyps in colonoscopy videos. The goal is to encourage more vigilance during procedures. The suggested polyp detection system consists of several novel modules: (1) a new patch descriptor that characterizes image appearance around boundaries more accurately and more efficiently than widely-used patch descriptors such HoG, LBP, and Daisy; (2) A 2-stage classification framework that is able to enhance low level image features prior to classification. Unlike the traditional way of image classification where a single patch undergoes the processing pipeline, our system fuses the information extracted from a pair of patches for more accurate edge classification; (3) a new vote accumulation scheme that robustly localizes objects with curvy boundaries in fragmented edge maps. Our voting scheme produces a probabilistic output for each polyp candidate but unlike the existing methods (e.g., Hough transform) does not require any predefined parametric model of the object of interest; (4) and a unique three-way image representation coupled with convolutional neural networks (CNNs) for classifying the polyp candidates. Our image representation efficiently captures a variety of features such as color, texture, shape, and temporal information and significantly improves the performance of the subsequent CNNs for candidate classification. This contrasts with the exiting methods that mainly rely on a subset of the above image features for polyp detection. Furthermore, this research is the first to investigate the use of CNNs for polyp detection in colonoscopy videos. The second component of our quality assurance system is an automatic image quality assessment for colonoscopy. The goal is to encourage more diligence during procedures by warning against hasty and low quality colon examination. We detect a low quality colon examination by identifying a number of consecutive non-informative frames in videos. We base our methodology for detecting non-informative frames on two key observations: (1) non-informative frames most often show an unrecognizable scene with few details and blurry edges and thus their information can be locally compressed in a few Discrete Cosine Transform (DCT) coefficients; however, informative images include much more details and their information content cannot be summarized by a small subset of DCT coefficients; (2) information content is spread all over the image in the case of informative frames, whereas in non-informative frames, depending on image artifacts and degradation factors, details may appear in only a few regions. We use the former observation in designing our global features and the latter in designing our local image features. We demonstrated that the suggested new features are superior to the existing features based on wavelet and Fourier transforms. The third component of our quality assurance system is a 3D visualization system. The goal is to provide colonoscopists with feedback about the regions of the colon that have remained unexamined during colonoscopy, thereby helping them improve their navigational skills. The suggested system is based on a new 3D reconstruction algorithm that combines depth and position information for 3D reconstruction. We propose to use a depth camera and a tracking sensor to obtain depth and position information. Our system contrasts with the existing works where the depth and position information are unreliably estimated from the colonoscopy frames. We conducted a use case experiment, demonstrating that the suggested 3D visualization system can determine the unseen regions of the navigated environment. However, due to technology limitations, we were not able to evaluate our 3D visualization system using a phantom model of the colon. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2015

abstract: Coronary computed tomography angiography (CTA) has a high negative predictive value for ruling out coronary artery disease with non-invasive evaluation of the coronary arteries. My work has attempted to provide metrics that could increase the positive predictive value of coronary CTA through the use of dual energy CTA imaging. After developing an algorithm for obtaining calcium scores from a CTA exam, a dual energy CTA exam was performed on patients at dose levels equivalent to levels for single energy CTA with a calcium scoring exam. Calcium Agatston scores obtained from the dual energy CTA exam were within ±11% of scores obtained with conventional calcium scoring exams. In the presence of highly attenuating coronary calcium plaques, the virtual non-calcium images obtained with dual energy CTA were able to successfully measure percent coronary stenosis within 5% of known stenosis values, which is not possible with single energy CTA images due to the presence of the calcium blooming artifact. After fabricating an anthropomorphic beating heart phantom with coronary plaques, characterization of soft plaque vulnerability to rupture or erosion was demonstrated with measurements of the distance from soft plaque to aortic ostium, percent stenosis, and percent lipid volume in soft plaque. A classification model was developed, with training data from the beating heart phantom and plaques, which utilized support vector machines to classify coronary soft plaque pixels as lipid or fibrous. Lipid versus fibrous classification with single energy CTA images exhibited a 17% error while dual energy CTA images in the classification model developed here only exhibited a 4% error. Combining the calcium blooming correction and the percent lipid volume methods developed in this work will provide physicians with metrics for increasing the positive predictive value of coronary CTA as well as expanding the use of coronary CTA to patients with highly attenuating calcium plaques. / Dissertation/Thesis / Ph.D. Bioengineering 2013

abstract: The objective of this small animal pre-clinical research project was to study quantitatively the long-term micro- and macro- structural brain changes employing multiparametric MRI (Magnetic Resonance Imaging) techniques. Two separate projects make up the basis of this thesis. The first part focuses on obtaining prognostic information at early stages in the case of Traumatic Brain Injury (TBI) in rat animal model using imaging data acquired at 24-hours and 7-days post injury. The obtained parametric T2 and diffusion values from DTI (Diffusion Tensor Imaging) showed significant deviations in the signal intensities from the control and were potentially useful as an early indicator of the severity of post-traumatic injury damage. DTI was especially critical in distinguishing between the cytotoxic and vasogenic edema and in identification of injury regions resolving to normal control values by day-7. These results indicate the potential of quantitative MRI as a clinical marker in predicting prognosis following TBI. The second part of this thesis focuses on studying the effect of novel therapeutic strategies employing dendritic cell (DC) based vaccinations in mice glioma model. The treatment cohorts included comparing a single dose of Azacytidine drug vs. mice getting three doses of drug per week. Another cohort was used as an untreated control group. The MRI results did not show any significant changes in between the two treated cohorts with no reduction in tumor volumes compared to the control group. The future studies would be focused on issues regarding the optimal dose for the application of DC vaccine. Together, the quantitative MRI plays an important role in the prognosis and diagnosis of the above mentioned pathologies, providing essential information about the anatomical location, micro-structural tissue environment, lesion volume and treatment response. / Dissertation/Thesis / Masters Thesis Bioengineering 2014

abstract: This work explores how flexible electronics and display technology can be applied to develop new biomedical devices for medical, biological, and life science applications. It demonstrates how new biomedical devices can be manufactured by only modifying or personalizing the upper layers of a conventional thin film transistor (TFT) display process. This personalization was applied first to develop and demonstrate the world's largest flexible digital x-ray detector for medical and industrial imaging, and the world's first flexible ISFET pH biosensor using TFT technology. These new, flexible, digital x-ray detectors are more durable than conventional glass substrate x-ray detectors, and also can conform to the surface of the object being imaged. The new flexible ISFET pH biosensors are >10X less expensive to manufacture than comparable CMOS-based ISFETs and provide a sensing area that is orders of magnitude larger than CMOS-based ISFETs. This allows for easier integration with area intensive chemical and biological recognition material as well as allow for a larger number of unique recognition sites for low cost multiple disease and pathogen detection. The flexible x-ray detector technology was then extended to demonstrate the viability of a new technique to seamlessly combine multiple smaller flexible x-ray detectors into a single very large, ultimately human sized, composite x-ray detector for new medical imaging applications such as single-exposure, low-dose, full-body digital radiography. Also explored, is a new approach to increase the sensitivity of digital x-ray detectors by selectively disabling rows in the active matrix array that are not part of the imaged region. It was then shown how high-resolution, flexible, organic light-emitting diode display (OLED) technology can be used to selectively stimulate and/or silence small groups of neurons on the cortical surface or within the deep brain as a potential new tool to diagnose and treat, as well as understand, neurological diseases and conditions. This work also explored the viability of a new miniaturized high sensitivity fluorescence measurement-based lab-on-a-chip optical biosensor using OLED display and a-Si:H PiN photodiode active matrix array technology for point-of-care diagnosis of multiple disease or pathogen biomarkers in a low cost disposable configuration. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2014

Development of state-of-the-art medical imaging modalities such as Magnetic Resonance Imaging, Computed Tomography, Galactography, MR Diffusion Tensor Imaging, and Tomosynthesis plays an important role for visualization and assessment of anatomical structures. Included among these structures are structures of branching topology such as the bronchial tree in chest computed tomography images, the blood vessels in retinal images and the breast ductal network in x-ray galactograms and the tubular bone patterns in dental radiography. Analysis of such images could help reveal abnormalities, assist in estimating a risk of diseases such as breast cancer and COPD, and aid in the development of realistic anatomy phantoms. This thesis aims at the development of a set of automated methods for the analysis of anatomical structures of tree and network topology. More specifically, the two main objectives include (i) the development of analysis framework to explore the association between topology and texture patterns of anatomical branching structures and (ii) the development of the image processing methods for enhanced visualization of regions of interest in anatomical branching structures such as branching nodes. / Computer and Information Science