University of Illinois Urbana-Champaign

Exploring image segmentation methods to segment tumors by training over a dataset marked by skilled professionals

Somani, Varun

Content Files
ECE499-Sp2017-somani.pdf

Permalink

https://hdl.handle.net/2142/97883

Description

Title
Exploring image segmentation methods to segment tumors by training over a dataset marked by skilled professionals
Author(s)
Somani, Varun
Contributor(s)
Forsyth, D. A.
Issue Date
2017-12
Keyword(s)
  • image segmentation
  • machine learning
  • lung CT
  • supervised learning
  • unsupervised learning
Date of Ingest
2017-08-28T18:08:07Z
Abstract
This research uses chest CT scan images of lung cancer patients to examine current methods in image segmentation in the context of tumor segmentation. Potential benefits of the research include faster processing and detection time for patients as well as allowing doctors to rapidly proceed with the requisite procedures. We use both supervised and unsupervised methods to segment images. In terms of supervised methods, we use neural networks and SVMs with various “kernel tricks.” In terms of unsupervised methods, we use K-means clustering and Otsu’s method. Neural network gave the best result while other methods tended to have inferior performance. The results suggest that there is a possibility of further developing neural networks to conclusively solve the problem.
Type of Resource
text
Genre of Resource
Other
Language
en
Permalink
http://hdl.handle.net/2142/97883

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