# Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods

arXiv: https://arxiv.org/abs/1312.7345 · HTML: https://mlsanity.com/paper/1312.7345/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2013.

## Built on by (most cited first, 8 of 8)

- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation](https://mlsanity.com/paper/2408.09687/) (2024)
- [Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis](https://mlsanity.com/paper/2301.06226/) (2023)
- [Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism](https://mlsanity.com/paper/2111.08708/) (2021)
- [LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion](https://mlsanity.com/paper/2305.00837/) (2023)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)
- [Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-scale Dependency Modeling](https://mlsanity.com/paper/2310.13604/) (2023)
- [IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation](https://mlsanity.com/paper/2310.20292/) (2023)

## Abstract (arXiv)

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice. In this paper, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thresholding methods. Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state-of-the-art methods.