Identification of Gene Function Using Image Analytics
A challenge in neuroscience is understanding the genetic bases of behavior; traditionally, the nematode Caenorhabditis elegans is used as a model organism for neural studies due to its simple neural structure and well known connectome. Our study aims to identify genes that modulate food related behaviors in nematodes by quantifying behavioral differences in mutant strains. In order to empirically quantify nematode behavior, we have developed methods for recording and tracking nematodes over long periods of time, as well as algorithms for extracting and analyzing information from the observational data.
Computational Methods for Chronic Fatigue Syndrome
The goal of the Chronic Fatigue Syndrome (CFS) project is to analyze existing data to come up with an empirical definition for Chronic Fatigue Syndrome that could be used to develop a homogenous research group from which to explore biological causes. Because 1) current definitions of CFS are based on medical consensus, and 2) CFS shares symptoms with many other disorders, it is difficult to accurately explore biological causes. Recently, a new definition came out for Systemic Exertion Intolerance Disease (SEID), which is supposed to replace CFS and is causing some interesting movement in the research.
Computer-aided Diagnosis for Lung Cancer
The Lung Nodule Database Consortium (LIDC) is a joint initiative among five institutions providing a dataset for researchers to conduct analysis and develop techniques to advance the state-of-art computer-aided diagnosis (CADx) and detection (CADe) of lung nodules using Computed Tomography (CT) scans. The dataset contains 1,018 patient CT series in which 2,669 nodules are identified, outlined and rated by up to four radiologists. Radiologists provide 5-point scale ratings for 8 intermediate semantic characteristics and a malignancy rating. The complex structure of this dataset provides opportunities to conduct compelling research in Image Processing and Analysis, Machine Learning, and Data Mining, solving problems relevant beyond the medical domain.
MedIX: Research Experience for Undergraduates (REU) Program
The Medical Informatics (MedIX) program is a National Science Foundation REU site hosted by two interdisciplinary laboratories: the Medical Informatics Laboratory at DePaul University and the Imaging Research Institute at the University of Chicago. The main objectives of the MedIX REU program are to encourage talented undergraduates to pursue graduate education and to expose students to interdisciplinary research, especially at the border of information technology and medicine.
For more information, please visit the MedIX REU site here.
Bridging the Gap between Human and Computer Interpretation of Similarity in the Medical Domain
Focused on reducing the semantic gap by investigating computer-based similarity measures and image features that are close to the human perception of similarity and encoded the visual content of an image similarly to the human vision.
Information Extraction from Radiology Reports for the Purpose of Automatic Semantic Annotation of Medical Images
Proposed the development of a full-featured and scalable IE system capable of converting free-text radiology reports to a structured format.
Computer-aided Diagnosis for Breast Masses
Proposed: 1) a novel automatic mass segmentation method for identifying the contour (boundary) of a mass from a suspicious region (ROI) in a mammogram; 2) A multiple segmentation approach which builds multiple weak segmentors for each ROI image by applying a set of different image enhancements for mass segmentation; and 3) An ensemble approach where multiple base-level classifiers are built as experts from different perspectives to predicate the class probabilities; then, utilizing another learning algorithm, a meta-level classifier combines the diagnoses to generate the final diagnosis for a suspicious mass.
Approached a patent database and its citations with theoretical models applied from statistical analysis and physics. Using the clustering techniques of complex networks, we searched for patterns of relationships between patents of different categories.
Argonne National Laboratory (ANL) funded research work within the context of the Department of Homeland Security (DHS) microbial forensics programs toward the development of statistically-based experimental designs, microarray image analysis and decision tools for the analysis of genotyping and single nucleotide polymorphism microarrays.