The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation

TL;DR

This study explores how various image pre- and post-processing techniques, including wavelet decomposition and local binary patterns, can enhance U-Net performance for skin lesion segmentation, demonstrating that wavelet transforms notably improve results.

Contribution

The paper introduces the use of wavelet decomposition and local binary patterns as augmentation techniques to improve U-Net segmentation accuracy on skin lesion images.

Findings

Wavelet decomposition improves U-Net performance.

Pre- and post-processing techniques alone are less effective.

Wavelet transforms outperform other image enhancement methods.

Abstract

Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for developing automated skin lesion segmentation. This paper investigates various pre- and post-processing techniques for improving the performance of U-Nets as measured by the Jaccard Index. The dataset provided as part of the "2017 ISBI Challenges on Skin Lesion Analysis Towards Melanoma Detection" was used for this evaluation and the performance of the finalist competitors was the standard for comparison. The pre-processing techniques employed in the proposed system included contrast enhancement, artifact removal, and vignette correction. More advanced image transformations, such as local binary patterns and wavelet decomposition, were also employed to augment the raw grayscale images used as network input features. While the performance of the proposed system fell short of the winners of the challenge, it was determined that using wavelet decomposition as an early transformation step improved the overall performance of the system over pre- and post-processing steps alone.