Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma.
M Hossein Jafari, Ebrahim Nasr-Esfahani, Nader Karimi, S M Reza Soroushmehr, Shadrokh Samavi, Kayvan Najarian
Author Information
M Hossein Jafari: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Ebrahim Nasr-Esfahani: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Nader Karimi: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
S M Reza Soroushmehr: Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA.
Shadrokh Samavi: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. samavi@mcmaster.ca. ORCID
Kayvan Najarian: Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA.
PURPOSE: Computerized prescreening of suspicious moles and lesions for malignancy is of great importance for assessing the need and the priority of the removal surgery. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation is accurate detection of lesion's region, i.e., segmentation of an image into two regions as lesion and normal skin. METHODS: In this paper, a new method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed, and then, its patches are fed to a convolutional neural network. Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is proposed for more accurate detection of a lesion's border. RESULTS: Our results indicate that the proposed method could reach the accuracy of 98.7% and the sensitivity of 95.2% in segmentation of lesion regions over the dataset of clinical images. CONCLUSION: The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
