BACKGROUND: 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. METHODS: In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods. CONCLUSION: 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.
BACKGROUND: 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. METHODS: In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods. CONCLUSION: 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.
Authors: Nabin K Mishra; Ravneet Kaur; Reda Kasmi; Jason R Hagerty; Robert LeAnder; Ronald J Stanley; Randy H Moss; William V Stoecker Journal: Skin Res Technol Date: 2019-03-14 Impact factor: 2.365
Authors: Samuel C Hames; Sudipta Sinnya; Jean-Marie Tan; Conrad Morze; Azadeh Sahebian; H Peter Soyer; Tarl W Prow Journal: PLoS One Date: 2015-01-23 Impact factor: 3.240