# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 2 Prior Research  
In recent years, several studies have been presented in computer-aided melanoma detection and diagnosis areas [18]. Pigmented skin lesion detection and diagnosis is a crucial and challenging task in dermoscopic images because some elements/factors could alter and corrupt the image, impairing the development of high-performance CADe systems. An accurate segmentation phase enabling the partition of the image into distinct and non-overlapping regions is a fundamental step for a computer-based automatic diagnosis [19]. Various methods have been indicated in literature for the segmentation phase [20; 21; 22]: Some of them are based on easy and fast computational procedures needing post-processing steps (thresholding, region- and edge-based approaches); other methods combine simple segmentation techniques or perform high-level procedures to avoid post-processing and to reach good performance in presence of low contrast lesion boundaries, too (soft computing methods, deformable models, and so on). Benefits and robustness of thresholding, and edge-based and region-based methods for the segmentation of dermoscopic images are exploited in [23]. Using active contours, the proposed method adopted adaptive thresholding to select an initial contour and then maximized the Kullback-Leibler divergence of the gray level distribution between lesion and skin to fit a curve to the lesion boundaries. As post-processing phase, lesion periphery was extracted to detect melanoma using image features that were based on local binary patterns. The region-based active contour was, also, the key technique for lesion outline delineation phase in [24]. This procedure, after a preprocessing step for hair and shading effect attenuation, convert the RGB image into gray level image by applying the particle swarm optimization algorithm for the selection of best coefficients. The obtained gray level image was then used as input for the multi-Otsu method, which provides initial contour for border detection using active contour. Also, in [25] a geometric deformable model was adopted to effectively differentiate a skin lesion from surrounding tissue. The preprocessing phase of the procedure in [25] detected hairs, taking into account the dermoscopic knowledge of hair shafts. A 2D-Gabor filter was used, whose parameters were tuned by considering the hair properties: The filter band-pass and orientation take into account the width and the direction of hair shafts, respectively. In the next phase, the implemented deformable model used a speed function which considered the lesion chrominance properties to stop the evolution at the lesion boundary. Automatic initialization of the initial contour and chrominance-based speed function made a robust and flexible segmentation possible. In contrast with a full automatic approach, the manual initialization was adopted in [26] both to reduce the preprocessing phase and to avoid a lowering of the algorithm performance when contiguous area of the initial curve included regions with very different colors from normal skin. The RGB color space was converted for the purpose of using the image color information to distinguish normal skin from lesions. Moreover, the differences in color channels were combined together for deformable model speed function and stopping criterion definition. With the aim of maximizing the discrimination between lesion and background skin pixels, color channels from various color spaces were analyzed in [27] for the development of an automatic border detection system. The procedure, after the identification of the most efficient color channels, proposed a two stages thresholding to identify firstly the core lesion adopting the Otsu thresholding method and then the edge lesion area by the implementation of a clustering-based histogram thresholding method on the optimal color channels previously determined. The combination of multiple color spaces was used in [28] for the identification of pixels belonging to skin after an equalization phase for the image contrast enhancement.