A strategy for blood vessels segmentation based on the threshold which combines statistical and scale space filter. Application to the study of angiogenesis.
This paper presents a strategy for segmenting blood vessels based on the threshold, which combines statistics and scale space filter. By incorporating statistical information, the strategy is capable of reducing over-segmentation. We propose a two-stage strategy which involves: (1) optimal selection of window size and (2) optimal selection of scale. We compared our strategy to two commonly used thresholding techniques. Experimental results showed that our method is much more robust and accurate. Our strategy suggested a modification to Otsu's method. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed segmentation technique is tested on manual segmentation, where segmentation errors less than 3% were observed. The work presented in this paper is a part of a global image analysis process. Therefore, these images will be subject to a further morphometrical analysis in order to diagnose and predict automatically malign tumors.
Digital Signal Processing Group, Institute of Cybernetics, Mathematics and Physics (ICIMAF), Calle 15 no. 551 e/CyD, CP 10 400 Ciudad de la Habana, Cuba. rrm@icmf.inf.cu
Roberto
R
Rodríguez
D000465:Algorithms; D001808:Blood Vessels / Q000033:anatomy & histology*; D003462:Cuba; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*