A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of selected computer interchange color spaces
ACM Transactions on Graphics (TOG)
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Feature selection based on the training set manipulation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Pattern Recognition Letters
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Objective: This paper presents an automatic method for the quantification of the development of cutaneous hemangiomas in digital images. Two measurements on digital images acquired during follow-up examinations are performed: (1) the skin area affected by the lesion is measured and (2) the change of the hemangioma during follow-up examinations called regression is determined. Current manual measurements exhibit inter- and intra-reader variation, which impedes precision and comparisons across clinical studies. The proposed automatic method aims at a more accurate and objective evaluation of the course of disease than the current clinical practice of manual measurement. Methods and material: The proposed method classifies individual pixels and calculates the area based on a ruler attached to the skin. For the regression detection follow-up images are registered automatically based on local gradient histograms. The method was evaluated on 90 individual images and a set of 4 follow-up series consisting of 3-4 examinations. Results: The absolute average error of the individual area measurements lies at 0.0775cm^2 corresponding to a variation coefficient of 8.82%. The measurement of the regression area provides an absolute average error of 0.1134cm^2 and a variation coefficient of 7.40 %. Conclusions: The results indicate that the proposed method provides an accurate and objective evaluation of the course of cutaneous hemangiomas. This is relevant for the monitoring of individual therapy and for clinical trials.