Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images
Autor: | Neda Zamani Tajeddin, Mostafa Jahanifar, Babak Mohammadzadeh Asl, Ali Gooya |
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Rok vydání: | 2019 |
Předmět: |
FOS: Computer and information sciences
Skin Neoplasms Databases Factual Computer science Property (programming) Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Dermoscopy Health Informatics 02 engineering and technology Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Health Information Management Discriminative model Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Segmentation Saliency map Electrical and Electronic Engineering Skin business.industry Pattern recognition Thresholding Computer Science Applications Random forest Feature (computer vision) 020201 artificial intelligence & image processing Supervised Machine Learning Artificial intelligence business Algorithms |
Zdroj: | IEEE Journal of Biomedical and Health Informatics. 23:509-518 |
ISSN: | 2168-2208 2168-2194 |
Popis: | Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI). DRFI method incorporates multi-level segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. In our improved saliency detection method, mDRFI, we have added some new features to regional property descriptors. Also, in order to achieve more robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the salient object in dermoscopic images. The proposed overall lesion segmentation framework uses detected saliency map to construct an initial mask of the lesion through thresholding and post-processing operations. The initial mask is then evolving in a level set framework to fit better on the lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed segmentation method outperforms the other conventional state-of-the-art segmentation algorithms and its performance is comparable with most recent approaches that are based on deep convolutional neural networks. Comment: ISIC2017, JBHI |
Databáze: | OpenAIRE |
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