6,691 research outputs found
Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation
The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Skin lesion segmentation is a fundamental task in dermoscopic image analysis.
The complex features of pixels in the lesion region impede the lesion
segmentation accuracy, and existing deep learning-based methods often lack
interpretability to this problem. In this work, we propose a novel unsupervised
Skin Lesion sEgmentation framework based on structural entropy and isolation
forest outlier Detection, namely SLED. Specifically, skin lesions are segmented
by minimizing the structural entropy of a superpixel graph constructed from the
dermoscopic image. Then, we characterize the consistency of healthy skin
features and devise a novel multi-scale segmentation mechanism by outlier
detection, which enhances the segmentation accuracy by leveraging the
superpixel features from multiple scales. We conduct experiments on four skin
lesion benchmarks and compare SLED with nine representative unsupervised
segmentation methods. Experimental results demonstrate the superiority of the
proposed framework. Additionally, some case studies are analyzed to demonstrate
the effectiveness of SLED.Comment: 10 pages, 8 figures, conference. Accepted by IEEE ICDM 202
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