Authors :
Linniya John
Volume/Issue :
Volume 6 - 2021, Issue 6 - June
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2UG27PG
Abstract :
The vascular network of human eye is having
variable size of blood vessels, contrast variations and
outlier presence in pathological cases. For the better
management of vascular diseases, automatic detection of
vessel structures is required which is of great importance
for its early diagnosis and treatment. A new model for
blood vessel segmentation from fundus images based on
stratified image matting is proposed. Generally, the image
matting models requires an input trimap. The trimap
generation is a time consuming task, also the accuracy of
segmentation results depends upon the quality of input
trimap. Therefore, a new method for generating a high
quality trimap in less time, based on the application of
kirsch template is incorporated in this image matting
model, for accurate matte estimation. First, the good
quality trimap is generated automatically by applying the
Kirsch template which includes three parts: Blood Vessel
Extraction Using Kirsch's Template, Co-fusion and Fuzzy
C-Means Clustering. Then, for better segmentation
performance, vessel pixels from unknown regions are
extracted using the multi-level image matting model.
Keywords :
Global Contrast Normalization (GCN), Conditional Random Field(CRF), Support Vector Machine, Patch Alignment Manifold Matting(PAMM), K-Nearest Neighbor, Fuzzy Multi-Criteria Evaluation (FMC).
The vascular network of human eye is having
variable size of blood vessels, contrast variations and
outlier presence in pathological cases. For the better
management of vascular diseases, automatic detection of
vessel structures is required which is of great importance
for its early diagnosis and treatment. A new model for
blood vessel segmentation from fundus images based on
stratified image matting is proposed. Generally, the image
matting models requires an input trimap. The trimap
generation is a time consuming task, also the accuracy of
segmentation results depends upon the quality of input
trimap. Therefore, a new method for generating a high
quality trimap in less time, based on the application of
kirsch template is incorporated in this image matting
model, for accurate matte estimation. First, the good
quality trimap is generated automatically by applying the
Kirsch template which includes three parts: Blood Vessel
Extraction Using Kirsch's Template, Co-fusion and Fuzzy
C-Means Clustering. Then, for better segmentation
performance, vessel pixels from unknown regions are
extracted using the multi-level image matting model.
Keywords :
Global Contrast Normalization (GCN), Conditional Random Field(CRF), Support Vector Machine, Patch Alignment Manifold Matting(PAMM), K-Nearest Neighbor, Fuzzy Multi-Criteria Evaluation (FMC).
