Welcome to Biomedical Imaging Informatics (BII) Laboratory

Image Processing and Maching Learning Methods
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-- The introduction of high-throughput scanning technology has allowed for routine digital 3D reconstruction of serial whole slide histology images. However, registration of serial whole slide images is a challenge due to the overwhelmingly large image size. A single image may contain several gigabytes of data. The resulting 3D volumes can easily exceed modern computer memory limits, thus precluding the direct use of existing reconstruction methods. Based on current methods, accessing subvolumes of tissues requires the prior registration of the entire full-resolution whole slide image volume, leading to a high computational cost. To solve this problem, we develop a hierarchical image registration method that computes image deformation on-the-fly. In this way, we can minimize the computational burden and focus analysis on the tissue subvolumes of interest to domain experts. As the nonrigid registration at the low resolution is applied to the rigidly registered image volume and only alignment transformations for all adjacent image pairs are available, we are developing a novel recursive mapping method that maps each pixel in the reference coordinate system to the corresponding location in a target image with use of the mapped nonrigid and rigid propagated transformations from low to high image resolution. In addition, we are also developing a multi-resolution, block-wise registration approach that works in a coarse-to-fine manner for multi-stain image registration.

-- A study on glioblastoma (GBM) cell’s invasive property is difficult with both in-vivo human patient and animal models. Therefore, GBM tumor resections are extracted from human brains with neurosurgery and grown in-vitro as GBM neurosphere cell lines instead. We have access to a spectrum of molecular representative human GBM cell lines with distinct biological behaviors. Motility evaluation is then conducted with cells in-vitro, which is prevalent in current research practice. However, such studies are limited by the fact that cells in-vitro can only migrate in a two-dimensional space. As a further step, such in-vivo cells from GBM cell lines are explanted to biologically relevant 3D scaffolds (200 μm in thickness) where live GBM cells in ex-vivo have three degrees of freedom for migration. Such an ex-vivo setting provides an opportunity to replicate the authentic brain environment, where cells interact with extracellular matrices as they migrate. With a three-dimensional spinning disc confocal image scanner (Nikon A1R) / a multiphoton imaging microscope (Zeiss 710 MP with a Chameleon Vision S Ti:Sapphire laser) for larger 3D structures (e.g. spheroids, 1 mm thick tissues), 3D fluorescent images of cell dynamics in the ex-vivo setting are acquired. We are developing 3D microscopy image analysis tools, with special focus on the development of methods to quantitatively measure the cell migration capacity deemed as a good indicator of the dramatic shift in tumor expansion property. The resulting image analysis workflow involves 3D cell segmentation, maximum cross-section detection, cell feature extraction, and 4D cell tracking. The resulting analysis workflow will be used to analyze and characterize ensemble cell invasion power in 3D space with cell lines under different molecular programs.


-- Fitting geometric models to objects of interest in images is one of the most classical problems studied in computer vision field. In addition, object representation is a common problem in diverse biomedical image processing applications in practice. As a result of its strong representation power and flexibility, conic is one of the geometric primitives widely used in a large number of image analysis applications. As opposed to most existing conic fitting methods minimizing the fitting error with the use of the second order polynomial representation, we haved contributed to this fundamental research area with a new ellipse-fitting framework from which two elegant ellipse-fitting algorithms have been derived. We propose a new method that formulates the geometric fitting problem as a process of seeking for the optimal mapping to a bivariate normal distribution model. As a result, some critical disadvantages tightly coupled with those methods following the routine polynomial representation can be well overcome. These algorithms are elegant with the mathematical formulation and novel by manifestation of the inherent connections between statistical models and ellipse fitters. With these new methods, objects of interests from numerous applications can be well characterized, ranging from cell and follicule head description in microscopy image analysis, stent profile reconstruction for cardiovascular operation assessment, to tissue biopsy representation for high throughput screening analysis.


-- Active contours, or more figuratively snakes, are a class of methods that search for and represent image features, usually object boundaries, with deformable contours driven by the net influence of both internal and external forces. The internal forces are designed in a way such that the contours are managed to maintain their tensions and rigidities, whereas the the external forces are derived from image data that encourage contours to conform to desired image features. We have developed a new pressure-like force that not only improves contour convergence rate, but also encourages contours to conform to concave regions. We introduce the steerable pressure force (SPF) for parametric active contour models by leveraging the original pressure force as its building block. Moreover, it distinguishes itself from the tradition pressure force with the great convenience of dynamically steering the direction of the pressure force to be conformed to the image content. Unlike the traditional pressure force, this new force does not require users’ input for the force direction and is steerable according to the image content. Better convergence rate as well as force normalization consistency of this new force are presented when compared with other commonly-used forces on synthetic images. Results on a MRI image smoothed at different levels demonstrate the robustness of this new force to noise.


-- As an effort to build an automated and objective system for pathologic image analysis, we have developed a self-reliant computerized image processing method for identifying nuclei, a basic biological unit of diagnostic utility, in microscopy images of glioma tissue samples. The complete analysis includes multiple processing steps, involving mode detection with color and spatial information for pixel clustering, background normalization leveraging morphological operations, boundary refinement with deformable models, and clumped nuclei separation. The developed analysis algorithm is sufficiently robust to considerable image variations inexorably coupled in microscopy images. Computerized nuclei detection results are in good concordance with human markups by both visual appraisement and quantitative measures. This suggests that the developed method is promising for generating quantitative and reliable analysis results to support further glioma analysis.


-- The diagnosis of diffuse gliomas requires the careful inspection of large amounts of visual data. Identifying tissue regions that inform diagnosis is a cumbersome task for human reviewers and is a process prone to inter-reader variability. To address this problem, we have developed an automatic method for identifying critical diagnostic regions within whole-slide microscopy images of gliomas. We frame the problem of critical region identification as a texture-based content retrieval task in the sense that each image is represented by a set of discriminating texture features. Both linear and nonlinear dimensionality reduction techniques are utilized to explore the intrinsic dimensionality of the feature space where images are classified by classification and regression trees with performances improved by a newly extended multi-class gentle boosting (MCGB) mechanism.


-- EMLDA is an iterative segmentation method utilizing the Fisher-Rao criterion as the kernel of the generic Expectation-Maximization (EM) algorithm. Linear Discriminant Analysis (LDA), a supervised classification technique, serves as the kernel of the EM-algorithm and iteratively groups data projected to a lower-dimensional feature space in such a way that the separability across all classes is maximized. In the E step, the “missing data” is estimated given the feature data and the model parameters of the current guess. In the M step, the model parameters are re-estimated in a way that maximize the criterion function using the updated “full data”.

Jun Kong, G. Teodoro, Y.H. Liang, Y.Y. Zhu, C. Tucker-Burden, F.S. Wang, D.J. Brat, Automated Cell Recognition with 3D Fluorescence Microscopy Images, International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp.1212-1215, Brooklyn, NY, April, 2015. (Oral)

Jun Kong, K. Boyer, J. Saltz and K. Huang, A New Model-based Estimation of Ellipses for Object Representation, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2009), pp.3637-3640, Minneapolis, MN, September 2009.

Jun Kong, Michael Lee, Pelin Bagci, Puneet Sharma, Diego Martin, N. Volkan Adsay, Joel Saltz, and Brad Farris, Computer-based Image Analysis of Liver Steatosis with Large-scale Microscopy Imagery and Correlation with Magnetic Resonance Imaging Lipid Analysis, IEEE International Conference of bioinformatics and biomedicine (BIBM), pp. 333-338, Atlanta, GA, November, 2011. (Oral; Acceptance rate for regular paper: 19%)

O. Sertel, Jun Kong, U.V. Catalyurek, G. Lozanski, J.H. Saltz and M. Gurcan, Histopathological Image Analysis for Follicular Lymphoma using Color Texture Models, Journal of Signal Processing Systems for Signal, Image, and Video Technology, DOI-10.1007/s11265-008-0201-y, Vol. 55, No. 1, pp.169-183, 2009.

O. Sertel, Jun Kong, G. Lozanski, U.V. Catalyurek, J.H. Saltz and M. Gurcan, Computerized microscopic image analysis of follicular lymphoma, Proc. SPIE Medical Imaging 2008, Vol. 6915, No. 1, 691535, San Diego, California, Feburary 2008. (Oral)

Jun Kong, Lee Cooper, Ashish Sharma, Tahsin Kurc, Daniel Brat, Joel Saltz, A new steerable pressure force for parametric deformable models, Proc. SPIE Medical Imaging 2011, Vol. 7962, 79623N, Orlando, Florida, February 2011.

Jun Kong, Lee Cooper, Tahsin Kurc, Daniel Brat, Joel Saltz, Towards Building Computerized Image Analysis Framework for Nucleus Discrimination in Microscopy Images of Diffuse Glioma, The 33rd International Conference of Engineering in Medicine and Biology Society(EMBC), pp. 6605-6608, Boston, MA, August, 2011. (Oral)

Jun Kong, Lee Cooper, Ashish Sharma, Tahsin Kurc, D. J. Brat and Joel H. Saltz, Texture Based Image Recognition in Microscopy Images of Diffuse Gliomas with Multi-class Gentle Boosting Mechanism, The 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp.457-460, Dallas, TX, March 2010. (Oral)

Jun Kong, O. Sertel, H. Shimada, K.L. Boyer, J.H. Saltz and M. Gurcan, Computeraided Evaluation of Neuroblastoma on Whole-slide Histology Images: Classifying Grade of Neuroblastic Differentiation, Journal of Pattern Recognition, Vol. 42, No. 6, pp.1080-1092, Jun 2009.

Jun Kong, H. Shimada, K.L. Boyer, J.H. Saltz and M. Gurcan, Image analysis for automated assessment of grade of neuroblastic differentiation, Proceedings of the Fourth IEEE International Symposium on Biomedical Imaging (ISBI 2007), pp.61-64, Metro Washington DC, April 2007.

Ongoing Microscopy Image Analysis for Liver Study









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-- Our research work also involves microscopy image analysis of steatosis area for liver transplantation assessment, as quantitating hepatic steatosis is important in many liver diseases and liver transplantation. Liver steatosis is an abnormal accumulation of lipid (fat) in liver cells. In liver transplantation, we need to quantify the degree of steatosis associated with post-transplant dysfunction. However, steatosis estimation by pathologists has inherent intra- and inter-observer variability. To address this problem, We have developed a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data.

-- Additionally, we compared and contrasted computerized techniques with magnetic resonance imaging measurements, pathologist visual scoring, and clinical parameters. Computerized methods applied to whole slide images included a commercial positive pixel count algorithm and our in-house image analysis method. For all liver samples, including pediatric, adult, frozen section, and permanent specimens, statistically significant correlations were observed between pathology, radiology, and each image analysis modality, with the strongest correlations in the pediatric cohort. Statistically significant relationships were observed between each method and with body mass index and with albumin but not with alanine aminotransferase or aspartate aminotransferase. Although pathologist assessments correlated, the absolute values of hepatic steatosis visual assessment were susceptible to intra- and inter-observer variability, particularly for microvesicular steatosis. Image analysis, pathologist assessments, radiology measurements, and several clinical parameters all showed correlations in this study, providing evidence for the utility of each method in different clinical and research settings.

-- As 2D microscopy images can only present 3D histopathological structures at discrete planes, they present significant information loss. Therefore, we are developing image analysis tools for 3D microscopy examiniations. This set of tools can derive accurate and informative 3D imaging features to help researchers and clinicians better analyze 3D biological structures. For example, we can calculate the inter-branch distance, characterize the branching pattern, and generate a histogram of branch lengths and thicknesses for the entire 3D tissue volume for vessel characterizations. We also aim to characterize the relationship between distinct types of structures in 3D space, for example, the average distance between a bile duct (or a cell) and its nearest blood vessel, to identify better clinically relevant patient stratification protocols and molecularly correlated phenotypic signature.

Jun Kong, Michael Lee, Pelin Bagci, Puneet Sharma, Diego Martin, N. Volkan Adsay, Joel Saltz, and Brad Farris, Computer-based Image Analysis of Liver Steatosis with Large-scale Microscopy Imagery and Correlation with Magnetic Resonance Imaging Lipid Analysis, IEEE International Conference of bioinformatics and biomedicine (BIBM), pp. 333-338, Atlanta, GA, November, 2011. (Oral; Acceptance rate for regular paper: 19%)

Michael J. Lee, Pelin Bagci, Jun Kong, Miriam B. Vos, Puneet Sharma, Bobby Kalb, JoelSaltz, Diego R. Martin, N. Volkan Adsay, Alton B. Farris, Liver Steatosis Assessment: Correlations Among Pathology, Radiology, Clinical Data and Automated Image Analysis Software, Pathology-Research and Practice, 209(6):pp.371-379, 2013.

M. Lee, P. Bagci, Jun Kong, M. Vos, V. Adsay, P. Sharma, D. Martin, A. Farris, Liver Steatosis Assessment: Correlations Between Pathology, Radiology, Clinical Data and Automated Image Analysis Software, The United States and Canadian Academy of Pathology’s 101st Annual Meeting, Vancouver, BC, Canada, March 2012.

Y.H. Liang, F.S. Wang, D. Treanor, D. Magee, G. Teodoro, Y.Y. Zhu, Jun Kong, Liver Whole Slide Image Analysis for 3D Vessel Reconstruction, International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp.182-185, Brooklyn, NY, April, 2015.

Y.H. Liang, F.S. Wang, D. Treanor, D. Magee, N. Roberts, G. Teodoro, Y.Y. Zhu, Jun Kong, A Framework for 3D Vessel Analysis using Whole Slide Images of Liver Tissue Sections, Accepted to International Journal of Computational Biology and Drug Design (IJCBDD) and International Conference on Intelligent Biology and Medicine (ICIBM), San Antonio, TX, 2014. (Travel Award)

In-Silico Integrative Research on Brain Tumor (Glioblastoma)








-- We synergized multi-resolution, multi-source data of brain tumors to better address scientific questions about disease onset and progression. We intensively made use of microscopy imaging data, MRI image features and omics data for large-scale correlation study. For imaging study particularly, we used the publicly available The Cancer Genome Atlas database for this study. Note some data can be accessed directly from the acquisition platforms, e.g. the molecular data. Others, e.g. the phenotypic information from microscopy images are only accessible after analysis. In short, we aim to extract anatomic characterization at cellular level of tissue pathology, integrate with multiple types of “omic” information, and create categories of jointly classified data to describe pathophysiology, predict prognosis and response to treatment.

-- To mitigate the low reproducibility and inter-observer agreement from human pathologic review process, we have developed a computerized image analysis to quantitatively and reproducibly measure histologic structures on a large-scale. We present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity.

-- Additionally, we have developed a methodology to subclassify disease with image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events. Analysis of glioblastoma identified three prognostically significant patient clusters, each characterized by molecular events in nuclear compartment signaling, developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.

Jun Kong, Lee A.D. Cooper, Fusheng Wang, Jingjing Gao, George Teodoro, Lisa Scarpace, TomMikkelsen, Carlos S.Moreno, Joel H. Saltz, Daniel J. Brat, Generic, Computer-based Morphometric Human Disease Classification Using Large Pathology Images Uncovers Signature Molecular Correlates, PLoS One, 8(11), e81049. doi: 10.1371/journal.pone.0081049, November, 2013.

Jun Kong, Fusheng Wang, George Teodoro, Lee Cooper, Carlos Moreno, Tahsin Kurc, Tony Pan, Joel Saltz, and Daniel Brat, High-Performance Computational Analysis of Glioblastoma Pathology Images with Database Support Identifies Molecular and Survival Correlates, IEEE International Conference of bioinformatics and biomedicine, pp.229-236, Shanghai, China, Decembe, 2013. (Oral; Acceptance rate for regular paper: 19.6%)

W. Caleb Rutledge, Jun Kong, Jingjing Gao, David Gutman, Lee Cooper, Christina Appin, Candace Chisolm, Yuna Park, Lisa Scarpace, Tom Mikkelsen, Mark Cohen, Ken Aldape, Roger McLendon, Norman Lehman, Ryan Miller, Matthew J. Schniederjan, Cameron Brennan, Joel H. Saltz, Carlos S. Moreno, Daniel J. Brat, Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and enriched in the mesenchymal transcriptional class, Clinical Cancer Research, 19(18): pp.4951-4960, September, 2013.

Fusheng Wang, Jun Kong, Jingjing Gao, David Alder, Lee Cooper, Cristobal Vergara-Niedermayr, Zhengwen Zhou, Bryan Katigbak, Tahsin Kurc, Daniel Brat, Joel Saltz, A High Performance Spatial Database Based Approach for Pathology Imaging Algorithm Evaluation, Journal of Pathology Informatics, 4(1), 2013.

Lee Cooper, Jun Kong, David A. Gutman, Fusheng Wang, Doris Gao, Christina Appin, Sharath R. Cholleti, Tony C. Pan, Ashish Sharma, Lisa Scarpace, Tom Mikkelsen, Tahsin Kurc, Carlos S. Moreno, Daniel J. Brat, Joel H. Saltz, Integrated Morphologic Analysis for the Identification and Characterization of Disease Subtypes, Journal of American Medical Informatics Association, 19(2):317-323, 2012.

Jun Kong, Lee A.D. Cooper, Fusheng Wang, David A. Gutman, Jingjing Gao, Candace Chisolm, Ashish Sharma, Tony C. Pan, Erwin G. Van Meir, Tahsin M. Kurc, Carlos S. Moreno, Joel H. Saltz and Daniel J. Brat, Integrative, Multi-modal Analysis of Glioblastoma Using TCGA Molecular Data, Pathology Images and Clinical Outcomes, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 12, pp. 3469-3474, Dec 2011. (article on journal cover page)

Jun Kong, Lee Cooper, Carlos Moreno, Fusheng Wang, Tahsin Kurc, Joel Saltz, Daniel Brat, In Silico Analysis of Nuclei in Glioblastoma using Large-scale Microscopy Images Improves Prediction of Treatment Response, The 33rd International Conference of Engineering in Medicine and Biology Society(EMBC), pp. 87-90, Boston, MA, August, 2011.

Jun Kong, Lee Cooper, Fusheng Wang, Candace Chisolm, Carlos Moreno, Tahsin Kurc, Patrick Widener, Daniel Brat, Joel Saltz, A Comprehensive Framework for Classification of Nuclei in Digital Microscopy Imaging: An Application to Diffuse Gliomas, The 8th International Symposium on Biomedical Imaging(ISBI), pp. 2128-2131, Chicago, Illinois, March, 2011. (Oral)

Lee Cooper, Jun Kong, Fusheng Wang, Tahsin Kurc, Carlos Moreno, Daniel Brat, Joel Saltz, Morphological Signatures and Genomic Correlates in Glioblastoma, The 8th International Symposium on Biomedical Imaging(ISBI), pp. 1624-1627, Chicago, Illinois, March, 2011. (Oral)

Lee Cooper, Jun Kong, David Gutman, Fusheng Wang, Sharath Cholleti, Tony Pan, Patrick Widener, Ashish Sharma, Tom Mikkelsen, Adam Flanders, Daniel Rubin, Erwin Van Meir, Tahsin Kurc, Carlos Moreno, Daniel Brat, Joel Saltz, An Integrative Approach for In Silico Glioma Research , IEEE Transactions on Biomedical Engineering, Vol. 57, No. 10, pp. 2617-2621, October 2010.

Computer-Aided Diagnosis Systems (applied to Neuroblastoma)






-- Neuroblastoma (NB)is one of the most frequently occurring cancerous tumors in children. Based on the International Neuroblastoma Pathology Classification defined by World Heath Organization, diagnosis of NBs involves the analysis of tumor differenitation grade and stroma degree.

-- The current pathology evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these image scan generate key quantifiable parameters and assist pathologists with accurate evaluations.

-- To automatically grade NB differentation, we developed a large set of image analysis techniques, applied them to histological images of haematoxylin and eosin (H&E) stained NB slides, and identified image regions associated with distinct differentiation grades, including undifferentiated, poorly-differentiated, differentiating grade.Texture features derived from segmented components of tissues were extracted and processed by anautomated classifier group trained with sampleimages with different grades of neuroblastic differentiation in a multi-resolution framework.

-- Addtionally, we proposed an image analysis system that classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. The resulting statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitize dwhole-slide images, we proposed a multi-resolution approach that mimics the way pathologists evaluates physical slides under microscope. The image analys starts from the lowest resolution and switches to higher resolutions when necessary. We employed an offline feature selection step, which determines the most discriminative features at each resolution level during the training step.

A. Ruiz, Jun Kong, M. Ujaklon, K.L. Boyer, J.H. Saltz and M. Gurcan, Pathological Image Segmentation for Neuroblastoma Using the GPU, Proceedings of the Fifth IEEE International Symposium on Biomedical Imaging (ISBI 2008), pp.296-299, Paris, France, May 2008. (Oral)

Jun Kong, O. Sertel, H. Shimada, K.L. Boyer, J.H. Saltz and M. Gurcan, Computeraided Evaluation of Neuroblastoma on Whole-slide Histology Images: Classifying Grade of Neuroblastic Differentiation, Journal of Pattern Recognition, Vol. 42, No. 6, pp.1080-1092, Jun 2009.

O. Sertel, Jun Kong, H. Shimada, U.V. Catalyurek, J.H. Saltz and M. Gurcan, Computeraided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development, Journal of Pattern Recognition, Vol. 42, No. 6, pp.1093-1103, Jun 2009.

Jun Kong, O. Sertel, H. Shimada, K.L. Boyer, J.H. Saltz and M.N. Gurcan, Computer assisted Grading of Neuroblastic Differentiation, Journal of Archive Pathology and Laboratory Medicine, Vol. 132, No. 6, pp. 903-904, June, 2008.

Jun Kong, O. Sertel, H. Shimada, K.L. Boyer, J.H. Saltz and M. Gurcan, A multiresolution image analysis system for computer-assisted grading of neuroblastoma differentiation, Proc. SPIE Medical Imaging 2008, Vol. 6915, No. 1, 69151T, San Diego, California, Feburary 2008.

O. Sertel, Jun Kong, H. Shimada, U.V. Catalyurek, J.H. Saltz and M.N. Gurcan, Computer-aided prognosis of neuroblastoma: Classification of stromal development on whole-slide images, Proc. SPIE Medical Imaging 2008, Vol. 6915, No. 1, 69150P, San Diego, California, Feburary 2008.

M. Gurcan, Jun Kong, O. Sertel, B.B. Cambazoglu, J.H. Saltz and U.V. Catalyurek, Computerized Pathological Image Analysis for Neuroblastoma Prognosis, Proceedings of the Annual Symposium of American Medical Informatics Association 2007 (AMIA 2007), pp.304-308, Chicago, IL, November 2007.

B.B. Cambazoglu, O. Sertel, Jun Kong, J.H. Saltz, M. Gurcan and U.V. Catalyurek, Efficient Processing of Pathological Images Using the Grid: Computer-Aided Prognosis of Neuroblastoma, Proceedings of the Fifth IEEE International Conference on Challenges of Large Applications in Distributed Environments (CLADE 2007), pp.35-41, Monterey Bay, CA, June 2007.

Jun Kong, O. Sertel, H. Shimada, K.L. Boyer, J.H. Saltz and M. Gurcan, Computeraided Grading of Neuroblastic Differentiation: Multi-resolution and Multi-classifier Approach, Proceedings of the IEEE International Conference on Image Processing(ICIP 2007), pp.525-528, San Antonio, TX, September 2007.

Jun Kong, H. Shimada, K.L. Boyer, J.H. Saltz and M. Gurcan, Image analysis for automated assessment of grade of neuroblastic differentiation, Proceedings of the Fourth IEEE International Symposium on Biomedical Imaging (ISBI 2007), pp.61-64, Metro Washington DC, April 2007.