Innovative approaches in tissue imaging in an in vivo setting have included the use of optical coherence tomography (OCT) as a substrate for providing high resolution images at depths approaching 1.5 mm. This technology has offered the possibility of analyzing many tissues that are presently only evaluated using histologic methods after excision or biopsy. Despite the relatively high penetration depths of OCT, it is unclear whether the images acquired approximately 0.5 mm beyond the tissue surface maintain sufficient resolution and signal-to-noise ratio to provide useful information. Furthermore, there are relatively few studies that evaluate whether advanced image processing can be harnessed to improve the effective depth capabilities of OCT in tissue. We tested a tissue phantom designed to mimic the prostate as a model system, and independently modulated its refractive index and transmittance. Using dynamic focusing, and with the aid of an image analysis paradigm designed to improve signal detection in a model of tissue, we tested potential improvements in the ability to resolve structures at increasing penetration depths. We found that co-registered signal averaging and wavelet denoising improved overall image quality. B-spline interpolation made it possible to integrate dynamic focus images in a way that improved the effective penetration depth without significant loss in overall image quality. These results support the notion that image processing can refine OCT images for improved diagnostic capabilities to support in vivo microscopy.
Quantifying and visualizing the shape of developing biological tissues provide information about the morphogenetic processes in multicellular organisms. The size and shape of biological tissues depend on the number, size, shape, and arrangement of the constituting cells. To better understand the mechanisms that guide tissues into their final shape, it is important to investigate the cellular arrangement within tissues. Here we present a data processing pipeline to generate 3D volumetric surface models of epithelial tissues, as well as geometric descriptions of the tissues’ apical cell cross-sections. The data processing pipeline includes image acquisition, editing, processing and analysis, 2D cell mesh generation, 3D contourbased surface reconstruction, cell mesh projection, followed by geometric calculations and color-based visualization of morphological parameters. In their first utilization we have applied these procedures to construct a 3D volumetric surface model at cellular resolution of the wing imaginal disc of Drosophila melanogaster. The ultimate goal of the reported effort is to produce tools for the creation of detailed 3D geometric models of the individual cells in epithelial tissues. To date, 3D volumetric surface models of the whole wing imaginal disc have been created, and the apicolateral cell boundaries have been identified, allowing for the calculation and visualization of cell parameters, e.g. apical cross-sectional area of cells. The calculation and visualization of morphological parameters show position-dependent patterns of cell shape in the wing imaginal disc. Our procedures should offer a general data processing pipeline for the construction of 3D volumetric surface models of a wide variety of epithelial tissues.
Background: Low reproducibility of histologic grading of breast carcinoma due to its subjectivity has traditionally
diminished the prognostic value of histologic breast cancer grading. The objective of this study is to
assess the effectiveness and reproducibility of grading breast carcinomas with automated computer-based image
processing that utilizes stochastic geometry shape analysis. Methods: We used histology images stained with
Hematoxylin & Eosin (H&E) from invasive mammary carcinoma, no special type cases as a source domain and
study environment. We developed a customized hybrid semi-automated segmentation algorithm to cluster the
raw image data and reduce the image domain complexity to a binary representation with the foreground representing
regions of high density of malignant cells. A second algorithm was developed to apply stochastic geometry
and texture analysis measurements to the segmented images and to produce shape distributions, transforming
the original color images into a histogram representation that captures their distinguishing properties between
various histological grades. Results: Computational results were compared against known histological grades
assigned by the pathologist. The Earth Mover's Distance (EMD) similarity metric and the K-Nearest Neighbors
(KNN) classification algorithm provided correlations between the high-dimensional set of shape distributions and
a priori known histological grades. Conclusion: Computational pattern analysis of histology shows promise as
an effective software tool in breast cancer histological grading.
Proc. SPIE. 4684, Medical Imaging 2002: Image Processing
KEYWORDS: Data modeling, Magnetic resonance imaging, Image segmentation, Heart, 3D modeling, Medical imaging, Motion models, Commercial off the shelf technology, 3D image processing, 3D magnetic resonance imaging
Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.
Diffusion weighted magnetic resonance imaging (DW MRI) is a technique that measures the diffusion properties of water molecules to produce a tensor-valued volume dataset. Because water molecules can diffuse more easily along fiber tracts, for example in the brain, rather than across them, diffusion is anisotropic and can be used for segmentation. Segmentation requires the identification of regions with different diffusion properties. In this paper we propose a new set of rotationally invariant diffusion measures which may be used to map the tensor data into a scalar representation. Our invariants may be rapidly computed because they do not require the calculation of eigenvalues. We use these invariants to analyze a 3D DW MRI scan of a human head and build geometric models corresponding to isotropic and anisotropic regions. We then utilize the models to perform quantitative analysis of these regions, for example calculating their surface area and volume.
This paper describes the major components of the grasp augmented vision system. Grasp is an object-oriented system written in C++, which provides an environment both for exploring the basic technologies of augmented vision, and for developing applications that demonstrate the capabilities of these technologies. The hardware components of grasp include video cameras, 6-D tracking devices, a frame grabber, a 3-D graphics workstation, a scan converter, and a video mixer. The major software components consist of classes that implement geometric models, rendering algorithms, calibration methods, file I/O, a user interface, and event handling.