A fast adaptive active contour model based on local gray difference for parotid duct.
10.12122/j.issn.1673-4254.2018.12.14
- Author:
Xuan DENG
1
;
Tianjun LAN
2
;
Minghui ZHANG
1
;
Zhifeng CHEN
3
;
Qian TAO
2
;
Zhentai LU
1
Author Information
1. Key Lab for Medical Imaging of Southern Medical University, Guangzhou 510515, China.
2. Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology Affiliated to Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou 510055, China.
3. Department of Stomatology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
active contour model;
fast self-adaptive;
image;
local gray difference;
local similarity factor;
parotid duct
- MeSH:
Algorithms;
Color;
Image Processing, Computer-Assisted;
Parotid Gland;
diagnostic imaging;
Salivary Ducts;
diagnostic imaging
- From:
Journal of Southern Medical University
2018;38(12):1485-1491
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To establish a fast adaptive active contour model based on local gray difference for parotid duct image segmentation.
METHODS:On the basis of the LBF model, we added the mean difference of the local gray scale inside and outside the contour as the energy term of the driving evolution curve, and the local gray-scale variance difference was used to replace and as the control term of the energy parameter value. Two local similarity factors of different neighborhood sizes were introduced to correct the effects of image gray unevenness and boundary blur to improve the segmentation efficiency.
RESULTS:During image segmentation, this algorithm allowed for adaptive adjustment of the evolution direction, velocity and the energy weight of the internal and external regions according to the difference of gray mean and variance between the internal and external regions. This algorithm was also capable of detecting the actual boundary in a complex gradient boundary region, thus enabling the evolution curve to approach the target boundary quickly and accurately.
CONCLUSIONS:The proposed algorithm is superior to the existing segmentation algorithms and allows fast and accurate segmentation of the parotid duct with well-preserved image details.
