Shape Deformation Statistics and Regional Texture-Based Appearance Models for Segmentation

Transferring identified regions of interest (ROIs) from planning-time MRI images to the trans-rectal ultrasound (TRUS) images used to guide prostate biopsy is difficult because of the large difference in appearance between the two modalities as well as the deformation of the prostate's shape caused by the TRUS transducer. This dissertation describes methods for addressing these difficulties by both estimating a patient's prostate shape after the transducer is applied and then locating it in the TRUS image using skeletal models (s-reps) of prostate shapes. First, I introduce a geometrically-based method for interpolating discretely sampled s-reps into continuous objects. This interpolation is important for many tasks involving s-reps, including fitting them to new objects as well as the later applications described in this dissertation. This method is shown to be accurate for ellipsoids where an analytical solution is known. Next, I create a method for estimating a probability distribution on the difference between two shapes. Because s-reps live in a high-dimensional curved space, I use Principal Nested Spheres (PNS) to transform these representations to instead live in a flat space where standard techniques can be applied. This method is shown effective both on synthetic data as well as for modeling the deformation caused by the TRUS transducer to the prostate. In cases where appearance is described via a large number of parameters, such as intensity combined with multiple texture features, it is computationally beneficial to be able to turn these large tuples of descriptors into a scalar value. Using the inherent localization properties of s-reps, I develop a method for using regionally-trained classifiers to turn appearance tuples into the probability that the appearance tuple in question came from inside the prostate boundary. This method is shown to be able to accurately discern inside appearances from outside appearances over a large majority of the prostate boundary. Finally, I combine these techniques into a deformable model-based segmentation framework to segment the prostate in TRUS. By applying the learned mean deformation to a patient's prostate and then deforming it so that voxels with high probability of coming from the prostate's interior are also in the model's interior, I am able to generate prostate segmentations which are comparable to state of the art methods.Doctor of Philosoph

Non-Euclidean classification of medically imaged objects via s-reps

AbstractClassifying medically imaged objects, e.g., into diseased and normal classes, has been one of the important goals in medical imaging. We propose a novel classification scheme that uses a skeletal representation to provide rich non-Euclidean geometric object properties. Our statistical method combines distance weighted discrimination (DWD) with a carefully chosen Euclideanization which takes full advantage of the geometry of the manifold on which these non-Euclidean geometric object properties (GOPs) live. Our method is evaluated via the task of classifying 3D hippocampi between schizophrenics and healthy controls. We address three central questions. 1) Does adding shape features increase discriminative power over the more standard classification based only on global volume? 2) If so, does our skeletal representation provide greater discriminative power than a conventional boundary point distribution model (PDM)? 3) Especially, is Euclideanization of non-Euclidean shape properties important in achieving high discriminative power? Measuring the capability of a method in terms of area under the receiver operator characteristic (ROC) curve, we show that our proposed method achieves strongly better classification than both the classification method based on global volume alone and the s-rep-based classification method without proper Euclideanization of non-Euclidean GOPs. We show classification using Euclideanized s-reps is also superior to classification using PDMs, whether the PDMs are first Euclideanized or not. We also show improved performance with Euclideanized boundary PDMs over non-linear boundary PDMs. This demonstrates the benefit that proper Euclideanization of non-Euclidean GOPs brings not only to s-rep-based classification but also to PDM-based classification

Calibration and Analysis of a Multimodal Micro-CT and Structured Light Imaging System for the Evaluation of Excised Breast Tissue.

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 ?m for a 1.2?min scan. The SLI system, spanning wavelengths 490?nm to 800?nm and spatial frequencies up to 1.37 , was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, and phase function parameter, ?, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7?mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS

Appearance normalization of histology slides

This paper presents a method for automatic color and intensity normalization of digitized histology slides stained with two different agents. In comparison to previous approaches, prior information on the stain vectors is used in the plane estimation process, resulting in improved stability of the estimates. Due to the prevalence of hematoxylin and eosin staining for histology slides, the proposed method has significant practical utility. In particular, it can be used as a first step to standardize appearance across slides and is effective at countering effects due to differing stain amounts and protocols and counteracting slide fading. The approach is validated against non-prior plane-fitting using synthetic experiments and 13 real datasets. Results of application of the method to adjustment of faded slides are given, and the effectiveness of the method in aiding statistical classification is shown

Skeletons, Object Shape, Statistics

Objects and object complexes in 3D, as well as those in 2D, have many possible representations. Among them skeletal representations have special advantages and some limitations. For the special form of skeletal representation called “s-reps,” these advantages include strong suitability for representing slabular object populations and statistical applications on these populations. Accomplishing these statistical applications is best if one recognizes that s-reps live on a curved shape space. Here we will lay out the definition of s-reps, their advantages and limitations, their mathematical properties, methods for fitting s-reps to single- and multi-object boundaries, methods for measuring the statistics of these object and multi-object representations, and examples of such applications involving statistics. While the basic theory, ideas, and programs for the methods are described in this paper and while many applications with evaluations have been produced, there remain many interesting open opportunities for research on comparisons to other shape representations, new areas of application and further methodological developments, many of which are explicitly discussed here

ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases, assess epidemiological associations, and evaluate the effects of pharmaceutical interventions. Processing HR-pQCT images has largely been supported using the scanner manufacturer scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The ORMIR_XCT Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows

Skeletal shape correspondence via entropy minimization

Cave 10 antechamber interior, north wall, west side lower niche; The Yungang Grottoes are located 16 km west of Datong and comprise about 53 caves and 51,000 statues. The site stretches about 1 kilometer from east to west and was hollowed from the sandstone cliffs of the Wuzhou Mountains during the 5th century CE under the patronage of the Northern Wei dynasty. Founded by the Tuoba or Toba people, who ruled northern China during the Northern and Southern Dynasties period (310-589 CE), the Wei dynasty adopted Buddhism as its state religion. Work was begun at Yungang by the emperor Wenchengdi (reigned 452-465). The caves range in width from 23 m to a few metres. In 494, the Wei moved their capital from Datong to Luoyang and the Yungang Grottoes slowly fell into decay. The Grottoes have been a UNESCO World Heritage site since 2001. Caves 7-12, which seem to be grouped in pairs, show a more regular plan than the Tanyao Caves. Rectangular, they are divided into front and rear chapels. The main image is accompanied by representations of bodhisattvas, triads of the Buddha, flying apsarasas (heavenly beings), heavenly musicians, jātakas (stories from the life of the historical Buddha) and ornamental reliefs covering the walls, ceilings and entrance. Source: Grove Art Online; http://www.oxfordartonline.com/ (accessed 5/9/2011