  Many automatic skin lesion diagnosis systems use segmentation as a
preprocessing step to diagnose skin conditions because skin lesion shape,
border irregularity, and size can influence the likelihood of malignancy. This
paper presents, examines and compares two different approaches to skin lesion
segmentation. The first approach uses U-Nets and introduces a histogram
equalization based preprocessing step. The second approach is a C-Means
clustering based approach that is much simpler to implement and faster to
execute. The Jaccard Index between the algorithm output and hand segmented
images by dermatologists is used to evaluate the proposed algorithms. While
many recently proposed deep neural networks to segment skin lesions require a
significant amount of computational power for training (i.e., computer with
GPUs), the main objective of this paper is to present methods that can be used
with only a CPU. This severely limits, for example, the number of training
instances that can be presented to the U-Net. Comparing the two proposed
algorithms, U-Nets achieved a significantly higher Jaccard Index compared to
the clustering approach. Moreover, using the histogram equalization for
preprocessing step significantly improved the U-Net segmentation results.
