###### Abstract
As we know, there are many traditional image segmentation algorithms such as thresholding, region growing, watersheds, active contour model, graph cuts, etc. [7]. However, these traditional image segmentation algorithms may not deal with noisy or blurred images well. In addition to traditional image segmentation algorithms, some machine-learning-based image segmentation algorithms have been used to develop CAD methods for dermatology. To date, some CAD methods for psoriasis images have been developed [3; 4; 5; 8; 9]. For example, [PERSON] et al. proposed a psoriasis segmentation method [4]. For a psoriasis image, the texture and color features were extracted and combined with a multiresolution-based signature subspace classifier for psoriasis segmentation. [PERSON] et al. presented an image processing algorithm with K-means clustering approach followed by morphological operations for psoriasis segmentation [5]. The K-means clustering algorithm was used to obtain the coarse segmentation, and then morphological operations were exploited to refine the coarse results. The authors of [3] developed a machine-learning-based method where a scaling contrast map and the texture features were measured and combined with a support vector machine (SVM), and the Markov random field was developed to identify scaling boundaries in psoriasis skin images. [PERSON] et al. proposed a CAD system for psoriasis image classification [8] where texture and color features were often extracted, and the authors combined a SVM classifier for psoriasis image classification using many existing methods. In the existing CAD system [8], the high-order spectra-based (HOS) features, texture features, and color features are extracted. To reduce the feature dimensions, principal component analysis (PCA) is used to find the dominant ones from these extracted features. The dominant features are used with a SVM classifier to achieve psoriasis image classification. Unfortunately, the existing method [8] seems not to be suitable for psoriasis segmentation. Since visual features in an image play an important role in many applications, researchers have paid more attention to visual feature extraction to solve the image classification, object detection, and image segmentation problems. Convolution Neural Networks (CNNs) [10; 11], one of deep learning models, have been demonstrated to be useful to learn the multilevel visual features from images. The main advantage of CNN is that strong features invariant to distortion and position at the same time can be extracted for image classification. Since 2012, the ability of the deep learning method AlexNet has  
Figure 1: Examples of unconstrained psoriasis images. been demonstrated for image classification compared with traditional machine-learning methods. In addition to AlexNet, there are some popular CNNs such as VGGNet, ResNet, GoogleNet, Inception, and DarkNet. For example, VGGNet stacks more than ten 3 \(\times\) 3 convolution layers to generate a deeper network. To reduce the effect of the vanishing gradient problem, the core idea of ResNet is the identity shortcut connection for designing a deeper network. It is expected that CNNs can be exploited to extract useful features to develop CAD methods for medical image analysis. For instance, an existing method [12] developed based on a sliding-window approach was proposed for psoriasis images. The authors of [12] used a CNN to extract useful features from a local rectangle region. Then, these features were exploited to determine whether one pixel was a psoriasis pixel. After evaluating each pixel based on these features, the psoriasis regions can be separated from the others in a psoriasis image. Though the existing method [12] can provide a better performance, its computational complexity is a little high. Actually, some state-of-the-art deep learning-based medical image segmentation methods such as the Fully Convolution Neural Network (FCN) and U-Net have been proposed [11; 13; 14; 15; 16; 17; 18; 19; 20].