Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours.
Comput Math Methods Med
; 2020: 6317415, 2020.
Article
in En
| MEDLINE
| ID: mdl-33204300
ABSTRACT
Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Interpretation, Computer-Assisted
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
Language:
En
Journal:
Comput Math Methods Med
Journal subject:
INFORMATICA MEDICA
Year:
2020
Document type:
Article