NDLI: Identification of lobar fissures in pathological lungs

Content Provider	IEEE Xplore Digital Library
Author	Wei, Q. Hu, Y. MacGregor, J.H. Gelfand, G.
Copyright Year	2010
Description	Author affiliation: Cardiovascular and Thoracic Surgical Services, Foothills Medical Center (FMC), Calgary, AB, CANADA (Gelfand, G.) \|\| Diagnostic Imaging, FMC, Calgary, AB, CANADA (MacGregor, J.H.) \|\| Dept. of ECE, U of C, Calgary, AB, CANADA (Hu, Y.) \|\| Dept. of Electrical and Computer Engineering (ECE), University of Calgary (U of C), AB, CANADA (Wei, Q.)
Abstract	Identification of lobar fissures in human lungs is a non-trivial task due to their variable shape and appearance, along with the low contrast and high noise in computed tomographic (CT) images. Pathologies in the lungs can further complicate this identification by deforming and/or disrupting the lobar fissures. Current algorithms rely on the general anatomy of the lungs to find fissures affected by pathologies. This can be unreliable as deformations and/or disruptions of these fissures will alter the general lung anatomy. To overcome this, we developed an algorithm with the following novelties: (1) a new application of neural network based texture analysis to generalize fissure regions; and (2) a new method of fissure surface identification. We tested our algorithm on CT image stacks from 8 anonymous patients with pathological lungs. Compared to manually segmented fissures, our algorithm produced an average mean difference of 0.71 mm and 0.68 mm for identifying the left and right oblique fissures, respectively. Using a 3-mm percentile measure, the algorithm yielded an average accuracy of 86.8% for the left oblique fissure with a mean worst-case error of 3.18 mm. For the right oblique fissure, the algorithm produced an accuracy of 88.8% with a mean worst-case error of 3.13 mm. The above results show feasibility of using our algorithm for identifying fissures in pathological lungs.
Starting Page	5347
Ending Page	5350
File Size	817121
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626469
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lungs Computed tomography Pathology Pixel Artificial neural networks Image segmentation Accuracy CT images segmentation lungs lobar fissures
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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