Current research projects

Research Laboratories

Intelligent Multimedia Processing (IMP)

Medical Informatics (MedIX)

Project 1: Computer-aided detection, diagnosis, and characterization for lung nodule interpretation
Early diagnosis and treatment of lung cancer offers hope for improving the outcomes of patients with this most common cause of cancer death. Early diagnosis depends upon the diagnosis of small pulmonary nodules by radiologists using high-resolution computed tomography (CT) imaging to detect and assess small focal anomalies. Although more sensitive than chest X-rays for imaging pulmonary nodules, CT requires radiologists to review tens to hundreds of images for every patient case. In the Intelligent Multimedia Processing (IMP) lab and the Medical Imaging Informatics Lab we are developing novel computer-aided approaches to address this workload challenge and to act as “second readers” in the interpretation process. Our main projects are in the areas of 1) computer-aided detection (CADe), 2) computer-aided diagnosis (CADx), and 3) computer-aided diagnostic characterization (CADc). For more information on these projects, please visit the Publications page of the IMP Lab website at: http://facweb.cs.depaul.edu/research/vc/publications/index.htm

Project 2: Bridging the gap between human and computer interpretation of similarity in the medical domain
Content-Based Image Retrieval (CBIR) aims to retrieve images relevant to the image query and has the potential to be used as a decision support tool for evidence-based medicine and case-based reasoning. However, as decision support tools, the CBIR systems must produce similarity rankings that agree with the similarity opinions of expert radiologists. The difference between the content-based similarity results and the human-based similarity is called the “semantic gap” in the imaging research community. Our work focuses on reducing this semantic gap by investigating computer-based similarity measures and image features that are close to the human perception of similarity and encode the visual content of an image similarly to the human vision. For more information on these projects, please visit the Publications page of the IMP Lab website at:
http://facweb.cs.depaul.edu/research/vc/publications/index.htm

Project 3: Information Extraction from Radiology reports for the purpose of automatic semantic annotation of medical images
Radiology reports are clinical texts that serve as a primary means of communication between the radiologist and the referring physician. In a radiology report, image findings are described and interpreted, and the report thus serves the purpose of semantically annotating images with relevant information needed by the referring physician. Historically, the lack of a standard and convenient means of semantically annotating pixel image data, coupled with the convenience of entering free text, has naturally led to de-coupled and unstructured semantic annotation of medical images. In this project, we propose the development of a full-featured and scalable IE system capable of converting free-text radiology reports to a structured format. The output of the system will also aid the creation of targeted image datasets and provide image artifact information thus assisting the imaging research community.

Project 4: Computer-aided diagnosis for breast masses
In the medical imaging research, many Computer-aided diagnosis (CADx) systems have been developed to classify masses in mammogram images. In this project, in order to advance the CAD state of the art, we propose: 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.

 

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NSF REU Program in Medical Informatics Program

The Medical Informatics (MedIX) program’s main objectives 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. All of the projects on which students will work are inspired by state-of-the-art research questions in imaging informatics ranging from traditional image processing (e.g. liver segmentation and computer-aided diagnosis, breast density assessment for cancer detection) to structured reporting and natural language processing of radiology reports, to workflow and process re-engineering to the application of data mining and ontology-based means for image annotation and markup (e.g. lung nodule detection and interpretation). The Program is sponsored by National Science Foundation since 2005 and is hosted by two interdisciplinary laboratories: the Medical Informatics Laboratory at DePaul University and the Imaging Research Institute at the University of Chicago. For more information, please visit the Program’s website at: http://facweb.cs.depaul.edu/research/vc/medix/index.htm

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Our research approaches a patent database and its citations with theoretical models applied from statistical analysis and physics.  Using the clustering techniques of complex networks, we search for patterns of relationships between patents of different categories.  The research will allow legal specialists to identify the authority and importance of particular patents and measure their difference across various categories (i.e., the extent of difference between biotech and software categories).  Using the citations of patents to identify “truly authoritative” patents across different years and categories, our research measures the value of innovations and their impact in various fields of industry.  Intellectual property specialists from DePaul’s College of Law contribute the analysis, guidance, and instruction necessary to successfully identify the measures. 

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Bioinformatics

Argonne National Laboratory (ANL) through its Biochip Technology Center funded $50,000 to the CDM’s Intelligent Multimedia Processing (IMP) Lab to support Image Processing and Data Analysis research work within the context of the Department of Homeland Security (DHS) microbial forensics programs. The IMP Lab will work towards the development of statistically-based experimental designs, microarray image analysis and decision tools for the analysis of genotyping and single nucleotide polymorphism microarrays.

Microarray Toolbox Download, click here

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There are two research laboratories where faculties and students perform visual computing researches. Both laboratories are situated in CDM building, rooms 720 and 719 at 243 South Wabash Avenue, Chicago IL, 60604-2302
 

Intelligent Multimedia Processing (IMP) Laboratory hosts the undergraduate and graduate students doing Visual Computing research at CDM.  The IMP lab facilities include six brand new high-end workstations boasting Intel P4 HT processors at 3.2 GHz and having large amounts of memory (1GB ~ 2GB).   These facilities were provided by CDM to encourage the efforts made by the VC group to promote undergraduate and interdisciplinary research.  The undergraduate activities that have been already established in the IMP lab include: 1) an annual orientation and research projects’ overview, 2) weekly meetings, 3) written reports, and 4) presentations to the Visual Computing research group seminar and the UPE undergraduate research seminar. 

Medical Informatics Processing (MedIX) Laboratory

The MedIX lab houses eight workstations providing workspace for eight full-time students.  The MedIX workstations have the latest P4 3.2 GHz HT processors 1GB of DDR each.   The lab was funded by CDM to support the NSF REU site on Medical Informatics at CDM.

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