Title :
Computer-Supported Angiogenesis Quantification Using Image Analysis and Statistical Averaging
Author :
Doukas, Charalampos N. ; Maglogiannis, Ilias ; Chatziioannou, Aristotelis A.
Author_Institution :
Dept. of Inf. & Commun. Syst. Eng., Univ. of the Aegean, Mytilene
Abstract :
Angiogenesis is a complex process, involving multiple crosstalks among tumor, endothelial, and stromal cells in order to establish a biochemical network for oxygen and nutrients supply, necessary for the promotion of tumor growth. In this sense, measuring angiogenic activity is considered an informative marker of tumor growth or its inhibition. One of the most popular testbeds for the study of angiogenesis is developing chick embryo and its chorioallantoic membrane (CAM). In this paper, an automated image analysis and statistical processing method for the extraction of features informative for the angiogenic process is proposed and a Web-based tool that provides an unbiased quantification of the microvessel density and growth in angiogenic CAM images is described. The applicability of the tool is tested in two datasets, concerning: 1) the quantification and subsequent detection of tumor growth at different stages of embryonic development and 2) the inhibitory effect of dexamethasone (i.e., an inhibitor of the angiogenesis phenomenon) over a series of CAM samples. Experimental results presented in this paper indicate the efficiency of the automated angiogenesis quantification method regarding both tumor growth and inhibition detection.
Keywords :
biochemistry; cellular biophysics; medical image processing; statistical analysis; tumours; angiogenic activity; biochemical network; chorioallantoic membrane; computer-supported angiogenesis quantification; endothelial cells; image analysis; inhibition detection; statistical averaging; stromal cells; tumor cells; tumor growth; Angiogenesis Quantification; Angiogenesis quantification; Feature Extraction; Image Analysis; Medical Imagine Processing; Statistical Averaging; feature extraction; image analysis; medical image processing; statistical averaging; Algorithms; Angiography; Animals; Artificial Intelligence; Chick Embryo; Data Interpretation, Statistical; Neovascularization, Pathologic; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
DOI :
10.1109/TITB.2008.926463