Learning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection

Abstract

Graph signal processing (GSP) is becoming a major tool in biomedical signal and image analysis. In most GSP techniques, graph structures and edge weights have been typically set via statistical and computational methods. More recently, graph structure learning methods offered more reliable and flexible data representations. In this work, we introduce a graph learning approach for melanoma detection in dermoscopic images based on two graph-theoretic representations: superpixel ensemble graphs (SEG) and superpixel hierarchy graphs (SHG). For these two types of graphs, superpixel maps of a skin lesion image are respectively generated at multiple levels without and with parentchild constraints among superpixels at adjacent levels, where each level corresponds to a subgraph with a different number of nodes (20, 40, 60, 80, or 100 nodes). Two edge weight assignment techniques are explored: handcrafted Gaussian weights and learned weights based on optimization methods. The graph nodal signals are assigned based on texture, geometric, and color superpixel features. In addition, the effect of graph edge thresholding is investigated by applying different thresholds (25%, 50%, and 75%) to prune the weakest edges and analyze the impact of pruning on the melanoma detection performance. Experimental evaluation of the proposed method is performed with different classifiers trained and tested on the publicly available ISIC2017 dataset. Data augmentation is applied to alleviate class imbalance by adding more melanoma images from the ISIC archive. The results show that learned superpixel ensemble graphs with textural nodal signals give the highest performance reaching an accuracy of 99.00% and an AUC of 99.59%.

Figures (6)

• Figure 1: Dermoscopic image samples from the ISIC$2017$ dataset for both melanoma lesions (a, b) and benign lesions (c, d).
• Figure 2: Superpixel graph construction: (a) a color graph signal with each bar length representing the signal value for the corresponding superpixel, (b) superpixel graph construction with Gaussian-kernel-based handcrafted weights, and (c) superpixel graph construction with majorization-minimization-based learned weights.
• Figure 3: A hierarchical superpixel image structure with $M=80$ superpixels at the most refined level $K=4$. (a) Superpixel ensemble graph (SEG) where superpixel maps are independently constructed. (b) Superpixel hierarchy graph (SHG) where every superpixel map results from the immediately more refined one by merging every pair of superpixels.
• Figure 4: Block diagram of the proposed melanoma detection system.
• Figure 5: Areas under the ROC curve (AUCs) for seven different classifiers trained on features of single-level graphs and superpixel ensemble graphs (SEG): (a) geometric graph signals and handcrafted weights, (b) texture graph signals and handcrafted weights, (c) color graph signals and handcrafted weights, (d) geometric graph signals and learned weights, (e) texture graph signals and learned weights, (f) color graph signals and learned weights.