David J. Foran

Unsupervised segmentation based on robust estimation and color active contour models

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L/sub 2/E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L/sub 2/E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.

Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set

Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMAs) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm that can reliably separate touching cells in hematoxylin-stained breast TMA specimens that have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach that utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and TMAs containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) that resulted in significant speedup over the C/C++ implementation.

Multiple Class Segmentation Using A Unified Framework over Mean-Shift Patches

Object-based segmentation is a challenging topic. Most of the previous algorithms focused on segmenting a single or a small set of objects. In this paper, the multiple class object-based segmentation is achieved using the appearance and bag of keypoints models integrated over mean-shift patches. We also propose a novel affine invariant descriptor to model the spatial relationship of keypoints and apply the elliptical Fourier descriptor to describe the global shapes. The algorithm is computationally efficient and has been tested for three real datasets using less training samples. Our algorithm provides better results than other studies reported in the literature.

Mechanical behavior of vessel wall: a comparative study of aorta, vena cava, and carotid artery.

AbstractWe have used incremental stress-strain curves to study the mechanical behavior of porcine aorta, carotid artery, and vena cava. Elastic and viscous stress-strain curves are composed of low and high strain regions that are approximately linear. Analysis of the low strain behavior is consistent with previous studies that suggest that the behavior is dominated by the behavior of elastic fibers, and that the collagen and elastic fibers are in parallel networks. At high strain, the behavior is different than that of skin where it is dominated by the behavior of the collagen fibers. The high strain behavior is consistent with a series arrangement of the collagen and smooth muscle; however, the arrangement of smooth muscle and collagen may be different in aorta than in the other vessels studied. It is concluded that the mechanical behavior of the vessel wall differs from the behavior of other extracellular matrices that do not contain smooth muscle. Our results indicate that at least some of the collagen fibrils in the media are in series with smooth muscle cells and this collagen-smooth muscle network is in parallel with parallel networks of collagen and elastic tissue in aorta, carotid artery, and vena cava. It is concluded that the series arrangement of collagen and smooth muscle may be important in mechanochemical transduction in vessel walls and that the exact quantity and arrangement of these components may differ in different vessels.

Image-guided decision support system for pathology

Abstract. We present a content-based image retrieval system that supports decision making in clinical pathology. The image-guided decision support system locates, retrieves, and displays cases which exhibit morphological profiles consistent to the case in question. It uses an image database containing 261 digitized specimens which belong to three classes of lymphoproliferative disorders and a class of healthy leukocytes. The reliability of the central module, the fast color segmenter, makes possible unsupervised on-line analysis of the query image and extraction of the features of interest: shape, area, and texture of the nucleus. The nuclear shape is characterized through similarity invariant Fourier descriptors, while the texture analysis is based on a multiresolution simultaneous autoregressive model. The system performance was assessed through ten-fold cross-validated classification and compared with that of a human expert. To facilitate a natural man-machine interface, speech recognition and voice feedback are integrated. Client-server communication is multithreaded, Internet-based, and provides access to supporting clinical records and video databases.

Therapeutic Starvation and Autophagy in Prostate Cancer: A New Paradigm for Targeting Metabolism in Cancer Therapy

Autophagy is a starvation induced cellular process of self‐digestion that allows cells to degrade cytoplasmic contents. The understanding of autophagy, as either a mechanism of resistance to therapies that induce metabolic stress, or as a means to cell death, is rapidly expanding and supportive of a new paradigm of therapeutic starvation.

Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy

The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in Java, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a gold standard database which exhibit spectral and spatial profiles which are most similar to a given query image.

A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics

The tissue microarray (TMA) technique enables researchers to extract small cylinders of tissue from histological sections and arrange them in a matrix configuration on a recipient paraffin block such that hundreds can be analyzed simultaneously. TMA offers several advantages over traditional specimen preparation by maximizing limited tissue resources and providing a highly efficient means for visualizing molecular targets. By enabling researchers to reliably determine the protein expression profile for specific types of cancer, it may be possible to elucidate the mechanism by which healthy tissues are transformed into malignancies. Currently, the primary methods used to evaluate arrays involve the interactive review of TMA samples while they are viewed under a microscope, subjectively evaluated, and scored by a technician. This process is extremely slow, tedious, and prone to error. In order to facilitate large-scale, multi-institutional studies, a more automated and reliable means for analyzing TMAs is needed. We report here a web-based prototype which features automated imaging, registration, and distributed archiving of TMAs in multiuser network environments. The system utilizes a principal color decomposition approach to identify and characterize the predominant staining signatures of specimens in color space. This strategy was shown to be reliable for detecting and quantifying the immunohistochemical expression levels for TMAs.

Automatic Image Analysis of Histopathology Specimens Using Concave Vertex Graph

Automatic image analysis of histopathology specimens would help the early detection of blood cancer. The first step for automatic image analysis is segmentation. However, touching cells bring the difficulty for traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably handle touching cells segmentation. Robust estimation and color active contour models are used to delineate the outer boundary. Concave points on the boundary and inner edges are automatically detected. A concave vertex graph is constructed from these points and edges. By minimizing a cost function based on morphological characteristics, we recursively calculate the optimal path in the graph to separate the touching cells. The algorithm is computationally efficient and has been tested on two large clinical dataset which contain 207 images and 3898 images respectively. Our algorithm provides better results than other studies reported in the recent literature.

Bimodal system for interactive indexing and retrieval of pathology images

We demonstrate the prototype of an image understanding based system to support decision making in clinical pathology. The system employs all four major low level vision queues (shape, texture, color, metric measures) in content-based retrieval of visual information. The reliability of the central module of the system, the fast color segmenter, makes possible on-line analysis of the query image. The user interface is bimodal (speech and mouse input), allowing a natural communication with the system.