Image Analysis Quantifying Microvessel Density in Renal Cell Carcinoma

Hyun Ju Choi

doi:10.4258/jksmi.2009.15.2.217

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Choi: Image Analysis Quantifying Microvessel Density in Renal Cell Carcinoma

Original Article

Journal of Korean Society of Medical Informatics

Journal of Korean Society of Medical Informatics 2009; 15(2): 217-225.

Published online: 30 June 2009

DOI: https://doi.org/10.4258/jksmi.2009.15.2.217

Image Analysis Quantifying Microvessel Density in Renal Cell Carcinoma

Hyun Ju Choi

Center for Image Analysis, Uppsala University, Sweden.

Corresponding Author: Hyun Ju Choi, Centre for Image Analysis, Uppsala University, Box 337, SE-751 05 Uppsala, Sweden. Tel: +46-18-471-3460, Fax: +46-18-55-3447, hyunju.choi77@gmail.com

Received 23 April 2008 Accepted 20 March 2009

Abstract

Objective

The most widely used method for quantifying new blood vessel growth in tumor angiogenesis is the determination of microvessel density, which is reported to be associated with tumor progression and metastasis, and a prognostic indicator of patient outcome. In this study, we propose a method for the determination of microvessel density by image analysis, to improve the accuracy and the objectivity of determination of the microvessel density.

Methods

Four-micron-thick tissue sections of renal cell carcinoma samples were stained immunohistochemically for CD34. The regions with a high degree of vascularization were selected by an expert for digitization. Each image was digitized as a 24-bits/pixel image file with a resolution of 640×480 pixels. First, segmentation of the microvessels based on pixel classification using color features in hybrid color space was performed. After use of a correction process for microvessels with discontinuities and separation of touching microvessels, we counted the number of microvessels for the microvessel density measurement.

Results

The result was evaluated by comparison with manual quantification of the same images. The comparison revealed that our computerized microvessel quantification was highly correlated with manual counting by a pathologist.

Conclusion

The results indicate that our method is better than the conventional computerized image analysis methods.

Keywords: Microvessel Density, Color Features, Image Analysis, Computer-assisted Diagnosis

Figures and Tables

Figure 1

Color distribution of an original image and the preprocessed image. (a) Original image, (b) Preprocessed image, (c) Color distribution of (a), (d) Color distribution of (c)

Figure 2

(a) Segmentation procedure by discriminant functions, (b) Result of segmentation

Figure 3

(a) Procedure used to connect discontinuous microvessels, (b) Procedure used to separate touching microvessels

Figure 4

Result of a regression analysis between our proposed method and manual quantification by an expert

Figure 5

An example of false-positive and false-negative

Table 1

Stepwise selection summary

Notes

This work was supported by the Korea Research Foundation Grant funded by the Korea Government (MOEHRD, Basic Research Promotion Fund) (KRF-2005-217-D00005)

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