Title :
Automatic Attribute Threshold Selection for Blood Vessel Enhancement
Author :
Kiwanuka, Fred N. ; Wilkinson, Michael H F
Author_Institution :
Johann Bernoulli Inst. for Math. & Comput. Sci., Univ. of Groningen, Groningen, Netherlands
Abstract :
Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute threshold parameters based on image segmentation, thresholding and data clustering techniques. Though several techniques perform well on blood-vessel filtering, the choice of technique appears to depend on the imaging mode.
Keywords :
blood vessels; feature extraction; filtering theory; image enhancement; image segmentation; medical image processing; pattern clustering; attribute filters; automatic attribute threshold selection; biomedical imaging; blood vessel enhancement; blood-vessel filtering; data clustering techniques; feature extraction; image segmentation; Biomedical imaging; Blood vessels; Entropy; Histograms; Manuals; Rats; Shape; Connected filters; attribute filters; automatic thresholding; blood-vessel enhancement; clustering; mathematical morphology;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.566