Authors:
Roberto Lloréns
;
Valery Naranjo
;
Miriam Clemente
;
Mariano Alcãniz
and
Salvador Albalat
Affiliation:
Universidad Politécnica de Valencia, Spain
Keyword(s):
Jaw tissues segmentation, Dental implantology, Fuzzy connectedness, Mathematical morphology.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
The success of an oral implant surgery is subject to accurate advance planning. For this purpose, it is fundamental that a computer-guided program provides all the available information in a reliable way. Therefore, to plan a suitable implant placement, an accurate segmentation of the tissues of the jaw is necessary. These tissues are the cortical bone, trabecular core and the mandibular canal. The accurate segmentation of the mandibular canal, along which the inferior alveolar nerve crosses the lower arch, is particularly important since an injury to the canal can result in lip numbness.
To this date, existing segmentation methods for the jaw requires high human interaction and/or don't achieve enough accuracy. Our overall aim is to develop an automatic method for the segmentation of the whole jaw, focusing our efforts on achieving very high accuracy and time efficiency. To this end, this paper presents an exhaustive evaluation of fuzzy connectedness object extraction as a plausibl
e segmentation core for this method, basing on the results achieved on 80 CT slices in terms of detection and false alarm probability and merit factor.
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