Evaluation of the Influence of Image Compression to the Automatic Discrimination of Histological Images of Skin Lesions

M. Wiltgen; A. Gerger; C. Wagner; P. Bergthaler; J. Smolle

doi:10.1055/s-0038-1633852

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2004; 43(02): 141-149
DOI: 10.1055/s-0038-1633852

Original Article

Schattauer GmbH

Evaluation of the Influence of Image Compression to the Automatic Discrimination of Histological Images of Skin Lesions

M. Wiltgen

¹Institute of Medical Informatics, Statistics and Documentation, University of Graz, Graz, Austria

,

A. Gerger

²Department of Dermatology, Division of Analytical-Morphological Dermatology, University of Graz, Graz, Austria

,

C. Wagner

¹Institute of Medical Informatics, Statistics and Documentation, University of Graz, Graz, Austria

,

P. Bergthaler

²Department of Dermatology, Division of Analytical-Morphological Dermatology, University of Graz, Graz, Austria

,

J. Smolle

²Department of Dermatology, Division of Analytical-Morphological Dermatology, University of Graz, Graz, Austria

› Author Affiliations

Abstract

Summary

Objectives: Telemedicine offers the possibility to get the opinion of an expert within a short time. To reduce data transfer via a network image compression is recommended. The disadvantage of error-free compression is a low compression rate. On the other hand an increased compression rate affects the information content of the image. In this study we evaluated the influence of the compression rate on the automatic discrimination of histological skin lesions in dermatopathology.

Methods: To be independent from subjective reviewing by a dermatopathologist we used tissue counter analysis (TCA) for automatic discrimination of skin tissue. TCA is based on the partition of the image into square elements where the features are calculated out of each square element. 40 cases of benign common nevi and 40 cases of malignant melanoma were used as the study set. First TCA was applied to the uncompressed images to check the discrimination power of the method. Then in the next steps the method was applied to the images with successively higher compression rates. For image compression the wavelet compression was used, where the compression rate was determined by neglecting wavelet coefficients with a magnitude below a given threshold. The number of remaining wavelet coefficients was used as criteria for the compression rate.

Results: This study shows that TCA allows automated discrimination even at higher compression rates where only 6-18% of the wavelet coefficients are used for image reconstruction. The recognition rate at higher compression is better for malignant melanoma than for benign common nevi.

Conclusion: The power of automated discrimination is not essential affected by wavelet image compression.

Keywords

Medical image processing - tissue counter analysis - image compression - teledermatology

Full Text

References

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