  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.
