  Melanoma is amongst most aggressive types of cancer. However, it is highly
curable if detected in its early stages. Prescreening of suspicious moles and
lesions for malignancy is of great importance. 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 of skin lesions is
accurate detection of lesion region, i.e. segmentation of an image into two
regions as lesion and normal skin. Accurate segmentation can be challenging due
to burdens such as illumination variation and low contrast between lesion and
healthy skin. In this paper, a 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 (CNN). 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 used for more accurate detection of a lesion border. The output segmentation
mask is refined by some post processing operations. The experimental results of
qualitative and quantitative evaluations demonstrate that our method can
outperform other state-of-the-art algorithms exist in the literature.
