Color image analysis for quantifying renal tumor angiogenesis.
OBJECTIVE
To segment and quantify microvessels in renal tumor angiogenesis based on a color image analysis method and to improve the accuracy and reproducibility of quantifying microvessel density.


METHODS
The segmentation task was based on a supervised learning scheme. First, 12 color features (RGB, HSI, I1I2I3 and L*a*b*) were extracted from a training set. The feature selection procedure selected I2L*S features as the best color feature vector. Then we segmented microvessels using the discriminant function made using the minimum error rate classification rule of Bayesian decision theory. In the quantification step, after applying a connected component-labeling algorithm, microvessels with discontinuities were connected and touching microvessels separated. We tested the proposed method on 23 images.


RESULTS
The results were evaluated by comparing them with manual quantification of the same images. The comparison revealed that our computerized microvessel counting correlated highly with manual counting by an expert (r = 0.95754). The association between the number of microvessels after the initial segmentation and manual quantification was also assessed using Pearson's correlation coefficient (r = 0.71187). The results indicate that our method is better than conventional computerized image analysis methods.


CONCLUSIONS
Our method correlated highly with quantification by an expert and could become a way to improve the accuracy, feasibility and reproducibility of quantifying microvessel density. We anticipate that it will become a useful diagnostic tool for angiogenesis studies.
Choi
Hyun-Ju
HJ
School of Computer Engineering, Inje University, Gimhae, Republic of Korea.
Choi
Ik-Hwan
IH
Cho
Nam-Hoon
NH
Choi
Heung-Kook
HK
D000465:Algorithms; D001185:Artificial Intelligence; D002292:Carcinoma, Renal Cell / Q000098:blood supply / Q000473:pathology*; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008833:Microcirculation / Q000473:pathology; D009389:Neovascularization, Pathologic / Q000473:pathology*; D012044:Regression Analysis; D013194:Staining and Labeling