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Proceedings Paper

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images
Author(s): Trushali Doshi; John Soraghan; Derek Grose; Kenneth MacKenzie; Lykourgos Petropoulakis
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142N (20 March 2015); doi: 10.1117/12.2081864
Show Author Affiliations
Trushali Doshi, Univ. of Strathclyde (United Kingdom)
John Soraghan, Univ. of Strathclyde (United Kingdom)
Derek Grose, NHS Greater Glasgow and Clyde (United Kingdom)
Kenneth MacKenzie, NHS Greater Glasgow and Clyde (United Kingdom)
Lykourgos Petropoulakis, Univ. of Strathclyde (United Kingdom)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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