SBIR/STTR Research Award

Detection of Lung Cancer via Registered Temporal Images

Award identifier d926f30fb160328aea1165dd75eeeba56d02cc1352134e9835b30aebad63dda0Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$215,820

Program and phase

SBIR · Phase I

Award year

2003

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
Caelum Research Corporation
Proposal award date
Not reported
Contract end date
Not reported
UEI
WEHJBK4MXSN8
Solicitation number
Not reported
Topic code
Not reported

Research abstract

DESCRIPTION (provided by applicant): Phase I Effective registration functions of temporal digital images for many radiographic applications have been long over due. In the first phase of this project, we propose to develop key segmentation methods for automatic delineation of anatomic structures in both chest radiography and thoracic CT. It is our strategy to launch this important project to develop clinical tools for temporal chest image viewing and diagnosis for both thoracic CT and chest radiography. Even though thoracic CT will be used as a primary imaging device, chest radiography will most likely also be taken for its low-cost and high-resolution in the longitudinal direction. Technically speaking, it is also important to gain experience in segmenting and registering objects in 2D space before cultivating registration of lung structures in the 3-D space, particularly from the implementation aspects.We hypothesize that by eliminating the unchanged lung structure and/or by comparing the differences between the temporal images with the computer-aided system, the radiologist can more effectively detect the cancer in the lung field. We have done some preliminary studies and have appreciated the potential of this approach. Initially, we will put our main effort in segmentation of ribs in chest radiography and segmentation of lung structures including large and medium size bronchi and blood vessels in thoracic CT. Our specific aims in the Phase I program include: (1) Automatic extraction of large anatomic structures in chest imaging; (2) Accurate delineation of posterior ribs and anterior ribs in chest radiography; (3) Automatic slice matching for two CT scans; (4) Segmentation of large and medium sized tree structures in the thoracic CT; and (5) Evaluation of the computer segmentation results using radiologists' drawings and confirmation.We will then move into the registration of the segmented chest structures for matching and alignment between the temporal pair. Subtraction and visual inspection of the final image presentation will be carefully designed for clinical use in the second phase of this project.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

14 SBIR/STTR records share this UEI (1988–2023). Reported award amounts total $2,477,363. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Health and Human Services, Department of Defense, Department of Homeland Security

Contractor profile

CAELUM RESEARCH CORPORATION

Historical federal award activity, when published, is on that contractor profile. Matched by UEI only.

Agency intelligence

Department of Health and Human Services

NAICS intelligence

This classification comes from the contractor profile, not from the SBIR award record.

Contractor industry classification NAICS 518210

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.