Texture analysis of corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classifiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) offers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly fit CL classifiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classification programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93:3% of 60 test images. The segmentation algorithm achieved a mean (± standard deviation) sensitivity and specificity of 0:87 ± 0:14 and 0:91 ± 0:05, respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable

Evaluation of anomaly detection capabilities using a non-orthogonal camera angle in pulse-phase thermography

Pulse-phase thermography (PPT) is widely used to nondestructively inspect internal defects in fiber reinforced polymers. However, the challenges using PPT for complex shapes is poorly documented in literature. Only small changes in the object distance have been considered. Complex parts can have significant variations in object distance and thus, in detected radiation. In this contribution, the effect of a non-orthogonal camera angle with respect to a flat sample, leading to varying object distances and an inhomogeneous sound background area in phasegrams, is investigated. Samples with artificial round and square defects of different sizes are positioned under varying angles with respect to the camera, representing geometric properties of complex parts. The construction of the thermographic system and the experimental setup to systematically vary the angle between camera and specimen is presented. We investigated the change of the signal-to-noise ratio (SNR) of artificial delaminations in PPT measurements under varying object distances. The SNR in a distance of 136 mm out of the focal plane is sufficiently high for image feature extraction. Phasegrams are exported to a colored representation, leading to a higher contrast in distinct color channels. An algorithm which extracts and merges defect information from three different color channels is developed. Challenging lighting conditions lead to a noisy background having artifacts. The developed filter performs better in defect detection and size quantification than a global or local threshold in grayscale phasegrams under those conditions

Development and Applications of Advanced Ultrasound Techniques for Characterization and Stimulation of Engineered Tissues

Mechanobiology is central in the development, pathology, and regeneration of musculoskeletal tissues, in which mechanical factors play important roles. Therefore, there is a need for methods to characterize the composition and mechanical properties of developing musculoskeletal tissues over time. Ultrasound elastographic techniques have been developed for noninvasive imaging of spatial heterogeneity in tissue stiffness. However, their application for quantitative assessment of tissue mechanical properties, especially viscoelastic properties, has not been exploited. Additionally, ultrasound energy may be used to apply mechanical stimulation to engineered constructs at the microscale, and thereby to enhance tissue regeneration. We have developed a multimode ultrasound viscoelastography (MUVE) system for assessing microscale mechanical properties of engineered hydrogels. MUVE uses focused ultrasound pulses to apply acoustic radiation force (ARF) to deform samples, while concurrently measuring sample dimensions using coaxial high frequency ultrasound imaging. We used MUVE to perform creep tests on agarose, collagen, and fibrin hydrogels of defined concentrations, as well as to monitor the mechanical properties of cell-seeded constructs over time. Local and bulk viscoelastic properties were extracted from strain-time curves through fitting of relevant constitutive models, showing clear differences between concentrations and materials. In particular, we showed that MUVE is capable of mapping heterogeneity of samples in 3D. Using inclusion of dense agarose microbeads within agarose, collagen and fibrin hydrogels, we determined the spatial resolution of MUVE to be approximately 200 μm in both the lateral and axial directions. Comparison of MUVE to nanoindentation and shear rheometry showed that our ultrasound-based technique was superior in generating consistent, microscale data, particularly for very soft materials. We have also adapted MUVE to generate localized cyclic compression, as a means to mechanically stimulate engineered tissue constructs at the microscale. Selected treatment protocols were shown to enhance the osteogenic differentiation of human mesenchymal stem cells in collagen-fibrin hydrogels. Constructs treated at 1 Hz at an acoustic pressure of 0.7 MPa for 30 minutes per day showed accelerated osteogenesis and increased mineralization by 10 to 30 percent, relative to unstimulated controls. In separate experiments, the ultrasound pulse intensity was increased over time to compensate for changes in matrix properties over time, and a 35 percent increase in mineralization was achieved. We also extended the application of a previously-developed spectral ultrasound imaging (SUSI) technique to an animal model for early detection of heterotopic ossification (HO). The quantitative information on acoustic scatterer size and concentration derived from SUSI was used to differentiate tissue composition in a burn/tenotomy mice model from the control model. Importantly, HO foci were detected as early as one week after injury using SUSI, which is 3-5 weeks earlier than when using conventional micro-computed tomography. Taken together, these results demonstrate that ultrasound-based techniques can non-invasively and quantitatively characterize viscoelastic properties of soft materials in 3D, as well as their composition over time. Ultrasound pulses can also be used to stimulate engineered constructs to promote musculoskeletal tissue formation. MUVE, SUSI, and ultrasound stimulation can be combined into an integrated system to investigate the roles of matrix composition, static mechanical environment, and dynamic mechanical stimuli in tissue regeneration, as well as the interactions of these factors and their evolution over time. Ultrasound-based techniques therefore have promising potential in noninvasively characterizing the composition and biomechanics, as well as providing mechanical intervention in native and engineered tissues as they develop over time.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144116/1/xho_1.pd

Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image

Owing to the inconsistent image quality existing in routine obstetric ultrasound (US) scans that leads to a large intraobserver and interobserver variability, the aim of this study is to develop a quality-assured, fully automated US fetal head measurement system. A texton-based fetal head segmentation is used as a prerequi- site step to obtain the head region. Textons are calculated using a filter bank designed specific for US fetal head structure. Both shape- and anatomic-based features calculated from the segmented head region are then fed into a random forest classifier to determine the quality of the image (e.g., whether the image is acquired from a correct imaging plane), from which fetal head measurements [biparietal diameter (BPD), occipital–frontal diam- eter (OFD), and head circumference (HC)] are derived. The experimental results show a good performance of our method for US quality assessment and fetal head measurements. The overall precision for automatic image quality assessment is 95.24% with 87.5% sensitivity and 100% specificity, while segmentation performance shows 99.27% (`0.26) of accuracy, 97.07% (`2.3) of sensitivity, 2.23 mm (`0.74) of the maximum symmetric contour distance, and 0.84 mm (`0.28) of the average symmetric contour distance. The statistical analysis results using paired t-test and Bland–Altman plots analysis indicate that the 95% limits of agreement for inter observer variability between the automated measurements and the senior expert measurements are 2.7 mm of BPD, 5.8 mm of OFD, and 10.4 mm of HC, whereas the mean differences are −0.038 ` 1.38 mm, −0.20 ` 2.98 mm, and −0.72 ` 5.36 mm, respectively. These narrow 95% limits of agreements indicate a good level of consistency between the automated and the senior expert’s measurements

A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images

The clinical challenge of percutaneous coronary interventions (PCI) is highly dependent on the recognition of the coronary anatomy of each individual. The classic imaging modality used for PCI is angiography, but advanced imaging techniques that are routinely performed during PCI, like optical coherence tomography (OCT), may provide detailed knowledge of the pre-intervention vessel anatomy as well as the post-procedural assessment of the specific stent-to-vessel interactions. Computational fluid dynamics (CFD) is an emerging investigational tool in the setting of optimization of PCI results. In this study, an OCT-based reconstruction method was developed for the execution of CFD simulations of patient-specific coronary artery models which include the actual geometry of the implanted stent. The method was applied to a rigid phantom resembling a stented segment of the left anterior descending coronary artery. The segmentation algorithm was validated against manual segmentation. A strong correlation was found between automatic and manual segmentation of lumen in terms of area values. Similarity indices resulted >96% for the lumen segmentation and >77% for the stent strut segmentation. The 3D reconstruction achieved for the stented phantom was also assessed with the geometry provided by X-ray computed micro tomography scan, used as ground truth, and showed the incidence of distortion from catheter-based imaging techniques. The 3D reconstruction was successfully used to perform CFD analyses, demonstrating a great potential for patient-specific investigations. In conclusion, OCT may represent a reliable source for patient-specific CFD analyses which may be optimized using dedicated automatic segmentation algorithms

Quantifying Atherosclerosis: IVUS Imaging For Lumen Border Detection And Plaque Characterization

The importance of atherosclerotic disease in coronary artery has been a subject of study for many researchers in the past decade. In brief, the aim is to understand progression of such a disease, detect plaques at risks (vulnerable plaques), and treat them selectively to prevent mortality and immobility. Consequently, several imaging modalities have been developed and among them intravascular ultrasound (IVUS) has been of particular interest since it provides useful information about tissues microstructures and images with sufficient penetration as well as resolution. In general, the ultimate goal is to provide interventional cardiologists with reliable clinical tools so they can identify vulnerable plaques, make decisions confidently, choose the most appropriate drugs or implant devices (i.e. stent), and stabilize them during catheterization procedures with minimal risk. In this work, we review existing atherosclerotic tissue characterization algorithms including the state-of-the-art virtual histology (VH) framework, which has been implemented in the Volcano (Rancho Cordova, CA) IVUS clinical scanners using 64-elements 20 MHz phased-array transducer. Initially, we intended to extend this technique for data acquired with 40 MHz single-element transducers. For this reason, we started acquiring in vitro IVUS data and studied involved challenges from specimen preparation toward classification. We observed inconsistency among extracted features along with transducer's spectral parameters (i.e. bandwidth, center frequency). This, in addition to infeasibility of construction of reliable training dataset due to heterogeneity of atherosclerotic tissues motivated us to develop an unsupervised texture-based atherosclerotic tissue characterization algorithm. We proposed a two-dimensional multiscale wavelet-based algorithm to expand IVUS backscattered signals and/or grayscale images onto orthogonal symmetric quadrature mirror filters (QMF) such as Lemarie-Battle. At the bottom of decomposition tree, we employed ISODATA to cluster enveloped detected features in an unsupervised fashion and classify atherosclerotic plaque constitutes into fibrotic, lipidic, calcified, and no tissues. For the first time, we studied numbers of factors that were necessary for extension of in vitro derived classifier for in vivo applications such as reliability of classified tissues behind arc of calcified plaques and effects of pressure changes as well as flowing blood on constructed tissue color maps, called prognosis histology (PH) images. The second half of this dissertation is devoted to automatic detection of lumen borders in IVUS grayscale images acquired with high frequency (40 MHz up) transducers where more scattering exhibited within lumen area that makes the problem of interest more challenging. We established our framework on three-dimensional expansion of IVUS sub-volumes onto orthonormal brushlet basis function. The rational behind our framework was presence of incoherent (i.e. blood) versus coherent (i.e. plaque, surrounding fat) textural patterns along pullback direction, which was motivated by what an interventional cardiologist does to locate the lumen border visually by going back and forth among IVUS frames. We studied the feasibility of brushlet analysis through filtering blood speckles and supervised classification of blood versus non-blood regions. Our preliminary study confirmed that the most informative features reside in the innermost cubes, representing low-frequency components in transformed domain. Finally, we explored that tissue responses to IVUS signals are proportionally preserved in brushlet coefficients and it enabled us to classify blood regions in complex brushlet space. Subsequently, we employed surface function actives (SFA) to estimate the lumen borders after regularization. In a comparison study, we quantified our results with two of existing algorithms, employing IVUS grayscale images acquired with 40 MHz and 45 MHz single-element transducers. Overall, our proposed algorithm outperformed and the resulting automated detected borders showed good correlation with manually traced borders by an expert

Multimodal breast imaging: Registration, visualization, and image synthesis

The benefit of registration and fusion of functional images with anatomical images is well appreciated in the advent of combined positron emission tomography and x-ray computed tomography scanners (PET/CT). This is especially true in breast cancer imaging, where modalities such as high-resolution and dynamic contrast-enhanced magnetic resonance imaging (MRI) and F-18-FDG positron emission tomography (PET) have steadily gained acceptance in addition to x-ray mammography, the primary detection tool. The increased interest in combined PET/MRI images has facilitated the demand for appropriate registration and fusion algorithms. A new approach to MRI-to-PET non-rigid breast image registration was developed and evaluated based on the location of a small number of fiducial skin markers (FSMs) visible in both modalities. The observed FSM displacement vectors between MRI and PET, distributed piecewise linearly over the breast volume, produce a deformed Finite-Element mesh that reasonably approximates non-rigid deformation of the breast tissue between the MRI and PET scans. The method does not require a biomechanical breast tissue model, and is robust and fast. The method was evaluated both qualitatively and quantitatively on patients and a deformable breast phantom. The procedure yields quality images with average target registration error (TRE) below 4 mm. The importance of appropriately jointly displaying (i.e. fusing) the registered images has often been neglected and underestimated. A combined MRI/PET image has the benefits of directly showing the spatial relationships between the two modalities, increasing the sensitivity, specificity, and accuracy of diagnosis. Additional information on morphology and on dynamic behavior of the suspicious lesion can be provided, allowing more accurate lesion localization including mapping of hyper- and hypo-metabolic regions as well as better lesion-boundary definition, improving accuracy when grading the breast cancer and assessing the need for biopsy. Eight promising fusion-for-visualization techniques were evaluated by radiologists from University Hospital, in Syracuse, NY. Preliminary results indicate that the radiologists were better able to perform a series of tasks when reading the fused PET/MRI data sets using color tables generated by a newly developed genetic algorithm, as compared to other commonly used schemes. The lack of a known ground truth hinders the development and evaluation of new algorithms for tasks such as registration and classification. A preliminary mesh-based breast phantom containing 12 distinct tissue classes along with tissue properties necessary for the simulation of dynamic positron emission tomography scans was created. The phantom contains multiple components which can be separately manipulated, utilizing geometric transformations, to represent populations or a single individual being imaged in multiple positions. This phantom will support future multimodal breast imaging work

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

Peer reviewe

High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component

Adaptive windowing in contrast-enhanced intravascular ultrasound imaging

Intravascular ultrasound (IVUS) is one of the most commonly-used interventional imaging techniques and has seen recent innovations which attempt to characterize the risk posed by atherosclerotic plaques. One such development is the use of microbubble contrast agents to image vasa vasorum, fine vessels which supply oxygen and nutrients to the walls of coronary arteries and typically have diameters less than 200 µm. The degree of vasa vasorum neovascularization within plaques is positively correlated with plaque vulnerability. Having recently presented a prototype dual-frequency transducer for contrast agent-specific intravascular imaging, here we describe signal processing approaches based on minimum variance (MV) beamforming and the phase coherence factor (PCF) for improving the spatial resolution and contrast-to-tissue ratio (CTR) in IVUS imaging. These approaches are examined through simulations, phantom studies, ex vivo studies in porcine arteries, and in vivo studies in chicken embryos. In phantom studies, PCF processing improved CTR by a mean of 4.2 dB, while combined MV and PCF processing improved spatial resolution by 41.7%. Improvements of 2.2 dB in CTR and 37.2% in resolution were observed in vivo. Applying these processing strategies can enhance image quality in conventional B-mode IVUS or in contrast-enhanced IVUS, where signal-to-noise ratio is relatively low and resolution is at a premium