Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma.
OBJECTIVE
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.


CONCLUSIONS
The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
M Hossein
MH
Jafari
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Ebrahim
E
Nasr-Esfahani
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Nader
N
Karimi
Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA.|Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA.
S M Reza
SMR
Soroushmehr
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. samavi@mcmaster.ca.|Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA. samavi@mcmaster.ca.
Shadrokh
http://orcid.org/0000-0003-3951-3770
S
Samavi
Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA.|Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Kayvan
K
Najarian
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008545:Melanoma / Q000000981:diagnostic imaging / Q000601:surgery*; D012878:Skin Neoplasms / Q000000981:diagnostic imaging / Q000601:surgery*
Convolutional neural network; Deep learning; Medical image segmentation; Melanoma excision; Skin cancer