Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Trushali Doshi; John Soraghan; Derek Grose; Kenneth MacKenzie; Lykourgos Petropoulakis

doi:10.1117/12.2081864

20 March 2015 Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Trushali Doshi, John Soraghan, Derek Grose, Kenneth MacKenzie, Lykourgos Petropoulakis

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94142N (2015) https://doi.org/10.1117/12.2081864
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78±0.04 and average root mean square error of 1.82±0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion.

Citation Download Citation

Trushali Doshi, John Soraghan, Derek Grose, Kenneth MacKenzie, and Lykourgos Petropoulakis "Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142N (20 March 2015); https://doi.org/10.1117/12.2081864

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Cancer

Magnetic resonance imaging

Tissues

Image segmentation

Edge detection

Throat

Detection and tracking algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years