Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images

TL;DR

This paper introduces an improved supervised saliency detection method, mDRFI, tailored for dermoscopic images to enhance lesion segmentation accuracy in melanoma detection systems.

Contribution

The paper proposes mDRFI, an enhanced saliency detection approach with new regional features and background descriptors, improving lesion detection in dermoscopic images.

Findings

mDRFI outperforms DRFI in lesion saliency detection

The segmentation framework achieves higher accuracy than conventional methods

Performance is comparable to recent deep learning approaches

Abstract

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.