z-logo
open-access-imgOpen Access
Automatic Quantification of Microvessels Using Unsupervised Image Analysis
Author(s) -
Petter Ranefall,
Kenneth Wester,
Christer Busch,
PerUno Malmström,
Ewert Bengtsson
Publication year - 1998
Publication title -
analytical cellular pathology
Language(s) - English
Resource type - Journals
eISSN - 2210-7185
pISSN - 2210-7177
DOI - 10.1155/1998/490585
Subject(s) - thresholding , artificial intelligence , histogram , computer vision , focus (optics) , segmentation , computer science , image segmentation , set (abstract data type) , pattern recognition (psychology) , connected component labeling , connected component , image (mathematics) , scale space segmentation , physics , optics , programming language
An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram. To be able to count the objects, fragmented objects are connected, all objects are filled, and touching objects are separated using a watershed segmentation algorithm. The method is fully automatic and robust with respect to illumination and focus settings. A test set consisting of images grabbed with different focus and illumination for each field of view, was used to test the method, and the proposed method showed less variation than the intraoperator variation using manual threshold. Further, the method showed good correlation to manual object counting (r = 0.80) on an other test set.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom