Title :
Contrast-Independent Curvilinear Structure Detection in Biomedical Images
Author :
Obara, Boguslaw ; Fricker, Mark ; Gavaghan, David ; Grau, Vicente
Author_Institution :
Oxford e-Res. Centre, Oxford Centre for Integrative Syst. Biol., Oxford, UK
fDate :
5/1/2012 12:00:00 AM
Abstract :
Many biomedical applications require detection of curvilinear structures in images and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here, we propose a contrast-independent approach to identify curvilinear structures based on oriented phase congruency, i.e., the phase congruency tensor (PCT). We show that the proposed method is largely insensitive to intensity variations along the curve and provides successful detection within noisy regions. The performance of the PCT is evaluated by comparing it with state-of-the-art intensity-based approaches on both synthetic and real biological images.
Keywords :
image segmentation; medical image processing; object detection; PCT; biomedical applications; biomedical image detection; contrast-independent curvilinear structure detection; phase congruency tensor; semiautomatic segmentation; Eigenvalues and eigenfunctions; Equations; Feature extraction; Mathematical model; Noise; Tensile stress; Vectors; Bioimage informatics; curvilinear structure; live-wire tracing; phase congruency tensor (PCT); Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2012.2185938