학술논문

Thin structure filtering framework with non-local means, Gaussian derivatives and spatially-variant mathematical morphology
Document Type
Conference
Source
2013 IEEE International Conference on Image Processing Image Processing (ICIP), 2013 20th IEEE International Conference on. :1237-1241 Sep, 2013
Subject
Signal Processing and Analysis
Noise
Three-dimensional displays
Morphology
Vectors
Angiography
Image segmentation
Thin object filtering
mathematical morphology
Hessian filtering
non-local means
angiography
Language
ISSN
1522-4880
2381-8549
Abstract
Thin structure filtering is an important preprocessing task for the analysis of 2D and 3D bio-medical images in various contexts. We propose a filtering framework that relies on three approaches that are distinct and infrequently used together: linear, non-linear and non-local. This strategy, based on recent progress both in algorithmic/computational and methodological points of view, provides results that benefit from the advantages of each approach, while reducing their respective weaknesses. Its relevance is demonstrated by validations on 2D and 3D images.