2009
DOI: 10.1007/s11548-009-0389-8
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Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor

Abstract: We provided an integrated solution for teeth classification in multi-slice CT datasets. In this regard, suggested segmentation technique was successful to separate teeth from each other. The employed WFD approach was successful to discriminate and numbering of the teeth in the presence of missing teeth. The solution is independent of anatomical information such as knowing the sequence of teeth and the location of each tooth in the jaw.

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Cited by 36 publications
(22 citation statements)
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“…A machine learning algorithm predicts the disease/species from these features. Munisami et al (2015) segmented leaf images with Otsu’s algorithm ( Baxi and Vala, 2013 ) and calculated leaf length, width, area, perimeter, hull area, axis length and centroid and applied a K-NN for prediction. The system was tested on 640 images of 32 different plant species.…”
Section: Introductionmentioning
confidence: 99%
“…A machine learning algorithm predicts the disease/species from these features. Munisami et al (2015) segmented leaf images with Otsu’s algorithm ( Baxi and Vala, 2013 ) and calculated leaf length, width, area, perimeter, hull area, axis length and centroid and applied a K-NN for prediction. The system was tested on 640 images of 32 different plant species.…”
Section: Introductionmentioning
confidence: 99%
“…For teeth detection, Lin et. al 4 and Hosntalab et al 5 proposed pixel-level segmentation methods based on traditional computer vision techniques, such as thresholding, histogram-based, and level set methods. They detected teeth with the recall (sensitivity) of 0.94 and 0.88 respectively.…”
mentioning
confidence: 99%
“…Image analysis was performed in ImageJ (NIH -National Institutes of Health) and MATLAB. For analysis of cell body and nuclei, images were split into individual channels from RGB (red 594 nm and green 488 nm, blue 350 nm) and thresholded utilizing the Otsu method (Vala and Baxi, 2013) to remove any uneven illumination. Any debris and unwanted artifacts were cropped from the image.…”
Section: Image Acquisition and Analysis Quantification Of Marker Posmentioning
confidence: 99%