Probabilistic estimation of mechanical properties of biomaterials using atomic force microscopy

Nanoindentation using contact-mode atomic force microscopy (AFM) has emerged as a powerful tool for effective material characterization of a wide variety of biomaterials across multiple length scales. However, the interpretation of force-indentation experimental data from AFM is subject to some debate. Uncertainties in AFM data analysis stems from two primary sources: The exact point of contact between the AFM probe and the biological specimen and the variability in the spring constant of the AFM probe. While a lot of attention has been directed toward addressing the contact-point uncertainty, the effect of variability in the probe spring constant has not received sufficient attention. In this paper, we report on an error-in-variables-based Bayesian change-point approach to quantify the elastic modulus of human breast tissue samples after accounting for variability in both contact point and the probe spring constant. We also discuss the efficacy of our approach to a wide range of hyperparameter values using a sensitivity analysis.

Content-based histopathology image retrieval using CometCloud

Background

The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance.

Results

The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers.

Conclusions

In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

Towards an Automated MEMS-based Characterization of Benign and Cancerous Breast Tissue using Bioimpedance Measurements

Micro-Electro-Mechanical-Systems (MEMS) are desirable for use within medical diagnostics because of their capacity to manipulate and analyze biological materials at the microscale. Biosensors can be incorporated into portable lab-on-a-chip devices to quickly and reliably perform diagnostics procedure on laboratory and clinical samples. In this paper, electrical impedance-based measurements were used to distinguish between benign and cancerous breast tissues using microchips in a real-time and label-free manner. Two different microchips having inter-digited electrodes (10 µm width with 10 µm spacing and 10 µm width with 30 µm spacing) were used for measuring the impedance of breast tissues. The system employs Agilent E4980A precision impedance analyzer. The impedance magnitude and phase were collected over a frequency range of 100 Hz to 2 MHz. The benign group and cancer group showed clearly distinguishable impedance properties. At 200 kHz, the difference in impedance of benign and cancerous breast tissue was significantly higher (3110 Ω) in the case of microchips having 10 µm spacing compared to microchip having 30 µm spacing (568 Ω).

High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.

RUNX2 is overexpressed in melanoma cells and mediates their migration and invasion

In the present study, we investigated the role of the transcription factor RUNX2 in melanomagenesis. We demonstrated that the expression of transcriptionally active RUNX2 was increased in melanoma cell lines as compared with human melanocytes. Using a melanoma tissue microarray, we showed that RUNX2 levels were higher in melanoma cells as compared with nevic melanocytes. RUNX2 knockdown in melanoma cell lines significantly decreased Focal Adhesion Kinase expression, and inhibited their cell growth, migration and invasion ability. Finally, the pro-hormone cholecalciferol reduced RUNX2 transcriptional activity and decreased migration of melanoma cells, further suggesting a role of RUNX2 in melanoma cell migration.

Parallel content-based sub-image retrieval using hierarchical searching

MOTIVATION

The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches.

METHOD

Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image.

RESULTS

The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%.

AVAILABILITY AND IMPLEMENTATION

Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.

A Semi-Automated Positioning System for contact-mode Atomic Force Microscopy (AFM)

Contact mode Atomic Force Microscopy (CM-AFM) is popularly used by the biophysics community to study mechanical properties of cells cultured in petri dishes, or tissue sections fixed on microscope slides. While cells are fairly easy to locate, sampling in spatially heterogeneous tissue specimens is laborious and time-consuming at higher magnifications. Furthermore, tissue registration across multiple magnifications for AFM-based experiments is a challenging problem, suggesting the need to automate the process of AFM indentation on tissue. In this work, we have developed an image-guided micropositioning system to align the AFM probe and human breast-tissue cores in an automated manner across multiple magnifications. Our setup improves efficiency of the AFM indentation experiments considerably. Note to Practitioners: Human breast tissue is by nature heterogeneous, and in the samples we studied, epithelial tissue is formed by groups of functional breast epithelial cells that are surrounded by stromal tissue in a complex intertwined way. Therefore sampling a specific cell type on an unstained specimen is very difficult. To aid us, we use digital stained images of the same tissue annotated by a certified pathologist to identify the region of interest (ROI) at a coarse magnification and an image-guided positioning system to place the unstained tissue near the AFM probe tip. Using our setup, we could considerably reduce AFM operating time and we believe that our setup is a viable supplement to commercial AFM stages with limited X-Y range.

Automated image interpretation and computer-assisted diagnostics

Much of the difficulty in reaching consistent evaluations of radiology and pathology imaging studies arises from subjective impressions of individual observers. Developing strategies that can reliably transform complex visual observations into well-defined algorithmic procedures is an active area of exploration which can advance clinical practice, investigative research and outcome studies. The literature shows that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Advanced imaging and computational tools could potentially enable investigators to detect and track subtle changes in measurable parameters leading to the discovery of novel diagnostic and prognostic clues which are not apparent by human visual inspection alone. The overarching objective of this book chapter is to provide readers with a summary of the origin, evolution and future directions for the fields of automated image interpretation and computer-assisted diagnostics. The chapter begins with a high-level overview of the fields of image processing, pattern recognition, and computer vision followed by a description of how these disciplines relate to the more comprehensive fields of computer-assisted diagnostics and image guided decision support. Throughout the remainder of the chapter we have supplied multiple illustrative examples demonstrating how recent advances and innovations in each of these areas have impacted clinical and research activities throughout pathology and radiology including high-throughput tissue microarray analysis, multi-spectral imaging, and image co-registration.

Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images

RATIONALE AND OBJECTIVES

The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer.

MATERIALS AND METHODS

Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival.

RESULTS

A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively).

CONCLUSIONS

Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.

The analysis of image feature robustness using cometcloud

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning

BACKGROUND

Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task.

RESULTS

Logistic regression was applied to features extracted from candidate segmented nuclei, including nuclear shape, texture, context, and gene copy number, in order to rank objects according to the likelihood of being an accurately segmented nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 and FRA2 genes were labeled with FISH probes. Three trained reviewers independently classified nuclei into three classes of segmentation accuracy. In man vs. machine studies, the automated method outperformed the inter-observer agreement between reviewers, as measured by area under the receiver operating characteristic (ROC) curve. Robustness of gene position measurements to boundary inaccuracies was demonstrated by comparing 1086 manually and automatically segmented nuclei. Pearson correlation coefficients between the gene position measurements were above 0.9 (p < 0.05). A preliminary experiment was conducted to validate the ranked retrieval in a test to detect cancer. Independent manual measurement of gene positions agreed with automatic results in 21 out of 26 statistical comparisons against a pooled normal (benign) gene position distribution.

CONCLUSIONS

Accurate segmentation is necessary to automate quantitative image analysis for applications such as gene repositioning. However, due to heterogeneity within images and across different applications, no segmentation algorithm provides a satisfactory solution. Automated assessment of segmentations by ranked retrieval is capable of reducing or even eliminating the need to select segmented objects by hand and represents a significant improvement over binary classification. The method can be extended to other high-throughput applications requiring accurate detection of cells or nuclei across a range of biomedical applications.

A fast, automatic segmentation algorithm for locating and delineating touching cell boundaries in imaged histopathology

BACKGROUND

Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms.

OBJECTIVES

In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging.

METHODS

It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm.

RESULTS

The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation.

CONCLUSION

The proposed segmentation algorithm can accurately detect and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation.

Pathology Imaging Informatics for Clinical Practice and Investigative and Translational Research

Pathologists routinely interpret gross and microscopic specimens to render diagnoses and to engage in a broad spectrum of investigative research. Multiple studies have demonstrated that imaging technologies have progressed to a level at which properly digitized specimens provide sufficient quality comparable to the traditional glass slides examinations. Continued advancements in this area will have a profound impact on the manner in which pathology is conducted from this point on. Several leading institutions have already undertaken ambitious projects directed toward digitally imaging, archiving, and sharing pathology specimens. As a result of these advances, the use of informatics in diagnostic and investigative pathology applications is expanding rapidly. In addition, the advent of novel technologies such as multispectral imaging makes it possible to visualize and analyze imaged specimens using multiple wavelengths simultaneously. As these powerful technologies become increasingly accepted and adopted, the opportunities for gaining new insight into the underlying mechanisms of diseases as well as the potential for discriminating among subtypes of pathologies are growing accordingly.

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.

A data model and database for high-resolution pathology analytical image informatics

BACKGROUND

The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system.

CONTEXT

This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs).

AIMS

(1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects.

SETTINGS AND DESIGN

The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming increasingly feasible for basic, clinical, and translational research studies to produce thousands of whole-slide images. Systematic analysis of these large datasets requires efficient data management support for representing and indexing results from hundreds of interrelated analyses generating very large volumes of quantifications such as shape and texture and of classifications of the quantified features.

MATERIALS AND METHODS

We have designed a data model and a database to address the data management requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines. The data model represents virtual slide related image, annotation, markup and feature information. The database supports a wide range of metadata and spatial queries on images, annotations, markups, and features.

RESULTS

We currently have three databases running on a Dell PowerEdge T410 server with CentOS 5.5 Linux operating system. The database server is IBM DB2 Enterprise Edition 9.7.2. The set of databases consists of 1) a TMA database containing image analysis results from 4740 cases of breast cancer, with 641 MB storage size; 2) an algorithm validation database, which stores markups and annotations from two segmentation algorithms and two parameter sets on 18 selected slides, with 66 GB storage size; and 3) an in silico brain tumor study database comprising results from 307 TCGA slides, with 365 GB storage size. The latter two databases also contain human-generated annotations and markups for regions and nuclei.

CONCLUSIONS

Modeling and managing pathology image analysis results in a database provide immediate benefits on the value and usability of data in a research study. The database provides powerful query capabilities, which are otherwise difficult or cumbersome to support by other approaches such as programming languages. Standardized, semantic annotated data representation and interfaces also make it possible to more efficiently share image data and analysis results.

ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology

OBJECTIVE AND DESIGN

The design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated.

RESULTS

The experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.

Constitutive Smad linker phosphorylation in melanoma: a mechanism of resistance to transforming growth factor-β-mediated growth inhibition

Melanoma cells are resistant to transforming growth factor-β (TGFβ)-induced cell-cycle arrest. In this study, we investigated a mechanism of resistance involving a regulatory domain, called linker region, in Smad2 and Smad3, main downstream effectors of TGFβ. Melanoma cells in culture and tumor samples exhibited constitutive Smad2 and Smad3 linker phosphorylation. Treatment of melanoma cells with the MEK1/2 inhibitor, U0126, or the two pan-CDK and GSK3 inhibitors, Flavopiridol and R547, resulted in decreased linker phosphorylation of Smad2 and Smad3. Overexpression of the linker phosphorylation-resistant Smad3 EPSM mutant in melanoma cells resulted in an increase in expression of p15(INK4B) and p21(WAF1) , as compared with cells transfected with wild-type (WT) Smad3. In addition, the cell numbers of EPSM Smad3-expressing melanoma cells were significantly reduced compared with WT Smad3-expressing cells. These results suggest that the linker phosphorylation of Smad3 contributes to the resistance of melanoma cells to TGFβ-mediated growth inhibition.

METAMERISM IN MULTISPECTRAL IMAGING OF HISTOPATHOLOGY SPECIMENS

A multispectral camera is capable of imaging a histologic slide at narrow bandwidths over the range of the visible spectrum. There is currently no clear consensus over the circumstances in which this added spectral data may improve computer-aided interpretation and diagnosis of imaged pathology specimens [1, 2, 3]. Two spectra which are perceived as the same color are called metamers, and the collection of all such spectra are referred to as the metamer set. Highly metameric colors are amenable to separation through multispectral imaging (MSI).Using the transformation between the spectrum and its perceived color, our work addresses the question of when MSI reveals information not represented by a standard RGB color image. An analytical estimate on the size of the metamer set is derived for the case of independent spectral absorption. It is shown that colors which are closest to the white point on the chromaticity diagram are highly metameric. A numerical method to estimate the metamer set in a domain-specific manner is provided. The method is demonstrated on multispectral data sets of imaged peripheral blood smears and breast tissue microarrays. An a priori estimate on the degree of metamerism from a standard color image is presented.

AN ADAPTIVE TRACKING ALGORITHM OF LUNG TUMORS IN FLUOROSCOPY USING ONLINE LEARNED COLLABORATIVE TRACKERS

Accurate tracking of tumor movement in fluoroscopic video sequences is a clinically significant and challenging problem. This is due to blurred appearance, unclear deforming shape, complicate intra- and inter- fractional motion, and other facts. Current offline tracking approaches are not adequate because they lack adaptivity and often require a large amount of manual labeling. In this paper, we present a collaborative tracking algorithm using asymmetric online boosting and adaptive appearance model. The method was applied to track the motion of lung tumors in fluoroscopic sequences provided by radiation oncologists. Our experimental results demonstrate the advantages of the method.

A multicore based parallel image registration method

Image registration is a crucial step for many image-assisted clinical applications such as surgery planning and treatment evaluation. In this paper we proposed a landmark based nonlinear image registration algorithm for matching 2D image pairs. The algorithm was shown to be effective and robust under conditions of large deformations. In landmark based registration, the most important step is establishing the correspondence among the selected landmark points. This usually requires an extensive search which is often computationally expensive. We introduced a nonregular data partition algorithm using the K-means clustering algorithm to group the landmarks based on the number of available processing cores. The step optimizes the memory usage and data transfer. We have tested our method using IBM Cell Broadband Engine (Cell/B.E.) platform.

A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications.

Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application.

A caGRID-ENABLED, LEARNING BASED IMAGE SEGMENTATION METHOD FOR HISTOPATHOLOGY SPECIMENS

Accurate segmentation of tissue microarrays is a challenging topic because of some of the similarities exhibited by normal tissue and tumor regions. Processing speed is another consideration when dealing with imaged tissue microarrays as each microscopic slide may contain hundreds of digitized tissue discs. In this paper, a fast and accurate image segmentation algorithm is presented. Both a whole disc delineation algorithm and a learning based tumor region segmentation approach which utilizes multiple scale texton histograms are introduced. The algorithm is completely automatic and computationally efficient. The mean pixel-wise segmentation accuracy is about 90%. It requires about 1 second for whole disc (1024×1024 pixels) segmentation and less than 5 seconds for segmenting tumor regions. In order to enable remote access to the algorithm and collaborative studies, an analytical service is implemented using the caGrid infrastructure. This service wraps the algorithm and provides interfaces for remote clients to submit images for analysis and retrieve analysis results.

3D SEGMENTATION OF THE LIVER USING FREE-FORM DEFORMATION BASED ON BOOSTING AND DEFORMATION GRADIENTS

This paper presents a novel automatic 3D hybrid segmentation approach based on free-form deformation. The algorithms incorporate boosting and deformation gradients to achieve reliable liver segmentation of Computed Tomography (CT) scans. A free-form deformable model is deformed under the forces originating from boosting and deformation gradients. The basic idea of the scheme is to combine information from intensity and shape prior knowledge to calculate desired displacements to the liver boundary on vertices of deformable surface. Boosting classifies the 3D image into a binary mask and the edgeflow generates a force field from the mask. The deformable surface deforms iteratively according to the force field. Deformation gradients cast restriction at each deformation step. The deformation converges to a stable status to achieve the final segmentation surface.