# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 2 Prior Research
A triangular tessellation of the edge of the detected skin lesion image was computed using Delaunay triangulation. The triangle area was in compliance with the extracted edge and, in fact, homogeneous and heterogeneous regions produced large and small triangles, respectively. A threshold was used for merging the regions by means of a normalized boundary cost. A level set-based segmentation method, which integrated texture features and distinguished the feature importance in response to different inner textures, was implemented in [29]. After a preprocessing step aimed at hair/black frame removing and potential lesion area detecting, the prior color probably distribution in the localized area and the Gabor wavelet-based features were extracted and integrated in the definition of the energy functional. For global optimization achieving, a Markov chain Monte Carlo sampling approach guided by the combined energy was adopted. Aiming to improve the algorithm performance, [PERSON] et al. [30] implemented two different segmentations on the image under test, adopting a probabilistic approach. After a lesion contrast enhancement by a stretching technique, which used low and high threshold values, the mean of the uniform distribution and the mean deviation of the normal distribution were evaluated on the enhanced image and their values were inserted in an activation function to perform two different segmentations. A fusion of segmented images was implemented by utilizing additive law of probability. To avoid sophisticated pre- and post-processing procedures, convolutional neural network was adopted in [31] for the development of a method able to handle dermoscopic images under various acquisition conditions. By exploiting 19-layer deep convolutional neural network (DFCN) with a little over 290,000 trainable parameters, the indicated system used a limited training dataset for an efficient learning. The DFCN architecture consisted of a convolutional path, in which contextual information was aggregated via convolution and pooling, and a deconvolution pathway in which the full image resolution was recovered by deconvolution and upsampling. To eliminate the need of sample reweighting, a novel loss function based on Jaccard distance, was adopted instead of the widely used cross entropy between predicted values and targets. The same loss function was used in [32] to implement an efficient segmentation procedure under limited computational resources. The method was based on fully convolutional neural network and consisted of an encoder-decoder structure. The encoder was composed of four dense blocks with each block having four layers, while the decoder had four blocks, with each block having three layers. High-resolution features from early layers as well as high-semantic features of deeper layers were processed by the network because multiple skip connections were arranged between encoder and decoder. Also, in [33] the fully convolutional neural network approach was adopted with multiple key improvements. In fact, the adoption of multistage fully convolution networks refined the skin lesion segmentation results across multiple stages. The complementary multistage segmentations were then integrated in parallel to obtain the final results. To ensure visual consistency in the segmented region boundaries for skin lesions characterized by fuzzy boundaries and/or low contrast to the background, [PERSON] et al. [34] adopted a deep class-specific learning and a probability-based stepwise integration of the derived class-specific segmentation maps. Based on the image classification probability, complementary segmentation results produced by different deep class-specific learning models were integrated to iteratively maximize label agreement between neighboring pixels. In [35], the deep learning approach was exploited to develop a resolution-independent segmentation method in dermoscopic images.