Automatic Segmentation of Skin Lesion Images Using Evolution Strategies

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Abstract
Skin cancer has been the most common and represents 50% of all new cancers de-
tected each year. If detected at an early stage, simple and economic treatment can
cure it mostly. Accurate skin lesion segmentation is critical in automated early skin
cancer detection and diagnosis systems. In this paper, we propose an Evolution
Strategies (ES) based segmentation algorithm to identify the lesion area within an
ellipse. The method is applied to 51 XLM and 60 TLM images which have manual
segmentation from dermatologists as TRUTH.




Introduction
Early detection of cancerous skin lesion has been agreed to be very im-
portant due to the wide spread of skin cancer as well as the economic and
successful treatment if detected early. For instance, malignant melanomas,
the deadliest form of all skin cancers, has cure rate of higher than 95% when
detected at an early stage [1]. Segmentation is essential in automatic skin
cancer detection and diagnosis systems. Zouridakis, et al.



Relevant works
Automated systems for detecting melanoma use one imaging modality (such
as dermoscopy), mathematical models, and computer algorithms to predict if a
skin lesion is melanoma [15]. The general steps of such a system include imag-
ing pre-processing, segmentation, feature extraction and calculation, and clas-
si¯cation. The main task of segmentation is to di®erentiate the lesion from the
background. Thresholding [2,19,26,18] and region growing are two simple and
most widely used algorithms in the literature.