DocumentCode
140376
Title
BiofilmQuant: A computer-assisted tool for dental biofilm quantification
Author
Mansoor, Awais ; Patsekin, Valery ; Scherl, Dale ; Robinson, J. Paul ; Rajwa, Bartlomiej
Author_Institution
Dept. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
fYear
2014
fDate
26-30 Aug. 2014
Firstpage
4244
Lastpage
4247
Abstract
Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In this paper, we introduce a new semi-automated software tool, BiofilmQuant, for dental biofilm quantification in quantitative light-induced fluorescence (QLF) images. The software uses a robust statistical modeling approach to automatically segment the QLF image into three classes (background, biofilm, and tooth substratum) based on the training data. This initial segmentation has shown a high degree of consistency and precision on more than 200 test QLF dental scans. Further, the proposed software provides the clinicians full control to fix any misclassified areas using a single click. In addition, BiofilmQuant also provides a complete solution for the longitudinal quantitative analysis of biofilm of the full set of teeth, providing greater ease of usability.
Keywords
biomedical optical imaging; dentistry; fluorescence; image segmentation; medical image processing; statistical analysis; QLF dental scans; QLF image; automatic segmentation; biofilm quant; computer-assisted tool; current gold standard; dental biofilm quantification; longitudinal quantitative analysis; manual delineation; microbial material; quantitative light-induced fluorescence imaging; robust statistical modeling approach; semiautomated software tool; state-of-the-art automatic biofilm methods; tooth substratum; training data; Biomedical imaging; Dentistry; Educational institutions; Image segmentation; Software; Teeth;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location
Chicago, IL
ISSN
1557-170X
Type
conf
DOI
10.1109/EMBC.2014.6944561
Filename
6944561
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