Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour Space
This work addresses skin cancer diagnosis by improving segmentation accuracy, but it is incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of automatic skin lesion segmentation for computer-aided diagnosis by applying GrabCut in HSV color space with minimal human interaction, achieving an average Jaccard Index of 0.71 on 1000 images from the ISIC 2017 dataset.
Skin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer. Vast variety in the appearance of the skin lesion makes this task very challenging. The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation with minimal human interaction in HSV color space. Preprocessing was performed for removing the outer black border. Jaccard Index was measured to evaluate the performance of the segmentation method. On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC challenge 2017 Training Dataset.