Optimized imaging prefiltering for enhanced image segmentation

TL;DR

This paper explores the use of the Box-Cox transformation as a preprocessing step to improve image segmentation, especially for traditional machine learning methods, by enhancing feature separability and efficiency.

Contribution

It demonstrates the effectiveness of Box-Cox transformation in image segmentation preprocessing, particularly for traditional machine learning models, with detailed evaluation and comparison.

Findings

Enhances feature separability in image segmentation

Improves computational efficiency for traditional methods

Deep learning models show inconsistent improvements

Abstract

The Box-Cox transformation, introduced in 1964, is a widely used statistical tool for stabilizing variance and improving normality in data analysis. Its application in image processing, particularly for image enhancement, has gained increasing attention in recent years. This paper investigates the use of the Box-Cox transformation as a preprocessing step for image segmentation, with a focus on the estimation of the transformation parameter. We evaluate the effectiveness of the transformation by comparing various segmentation methods, highlighting its advantages for traditional machine learning techniques-especially in situations where no training data is available. The results demonstrate that the transformation enhances feature separability and computational efficiency, making it particularly beneficial for models like discriminant analysis. In contrast, deep learning models did not show consistent improvements, underscoring the differing impacts of the transformation across model types and image characteristics.