Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
arXiv:1312.7345 · doi:10.1111/j.1600-0846.2012.00636.x
Abstract
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.
8 pages, 3 figures, 2 tables. arXiv admin note: substantial text overlap with arXiv:1009.1362
References in corpus (1)
Cited by in corpus (8)
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- Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
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- Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
- Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism
- Accessible Melanoma Detection using Smartphones and Mobile Image Analysis
- Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification