Thesis Topic Details

Topic ID:
22
Title:
Recognising Lung features in HRCT
Supervisor:
Arcot Sowmya
Research Area:
Artificial Intelligence
Associated Staff
Assessor:
Mike Bain
Topic Details
Status:
Active
Type:
R & D
Programs:
BINF BIOM CE CS SE
Group Suitable:
No
Industrial:
Pre-requisites:
AI course desirable
Description:
This project is part of a larger medical imaging project. The topic of interest is diseases of the lung, viewed primarily through high resolution computerized tomography (HRCT) images, which is a radiologic assessment technique that has become prominent in the last decade. HRCT scans help to detect many lung disease processes that might escape other tests. There are many variations in the images, depending on the stage of the disease and the range of genetic and environment factors. The project goal is to extract features of interest to radiologists, guided by existing medical knowledge. The techniques to be used include segmenting the image into regions of interest, computing region features and applying machine learning and clustering techniques cooperatively, in order to build recognition systems. You will work with real digital HRCT images from a radiology practice.
Comments:
--
Past Student Reports
  Sneha GOTETI in s2, 2013
Recognising Lung features in HRCT
 

Download report from the CSE Thesis Report Library

NOTE: only current CSE students can login to view and select reports to download.