# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 7 Discussions and Conclusions  
The adoption of CADe in the medical environment for the detection and classification of abnormalities as soon as they occur can be a valid support for specialists, especially during the screening phase when both a large amount of diagnostic examinations have to be referred and when diagnostic images are challenging to analyze. In this paper, a method to perform an automatic and accurate segmentation of pigmented skin lesion in dermoscopic images was presented. For benchmarking purpose, the conceived method was tested, adopting the publicly available PH\({}^{2}\) database. The possibility of adopting a variety of databases as test bench and the existence of several metrics for the procedure performance evaluation made the comparison among the several methods indicated in literature a hard task. The training phase of a computer method (i.e., feature selection and classifier training) as well as the reached results heavily depended on the characteristics of images composing the selected dataset in terms of resolution, contrast, lightness, sharpness, noise, and so on. Moreover, the provided public databases, show images differently: In the same instance, regions of interest comprised the lesion with its proximity, while others contained the single lesion or only part of the lesion crossing its margin. For these reasons, it was difficult to develop an \"optimal algorithm\", which is able to achieve the same results independently from the database adopted to test the conceived procedure. The PH\({}^{2}\) database used to test the proposed segmentation algorithm is very challenging. In fact, several images are heavily affected by poor illumination and by the presence of hair artifacts, some others contain multiple lesions, various lesions are connected to the boundary edge of the provided image, and a number of skin lesions occupy more than half of the image area, making the matrix computation inconsistent. For these reasons, many studies indicated in literature discard up to 40 images of the database to avoid images having the issues stated above [26; 29; 35; 49; 50]. Of course, the removal of the most critical database images during the test phase contributes to increase the procedure performance. Segmentation performance of the implemented, fully automatic method was quantitatively assessed by likening the results obtained to the ground truth provided by the PH\({}^{2}\) database. The ground truth was obtained by a manual segmentation of skin lesion provided by several experts. Since visual inspection of a lesion area by dermatologists depends on human vision and physician experience and expertise, manual segmentation is subjective and nonreproducible [5]. In fact, different specialists produce distinct segmentations when delineating the same lesion and sometimes the segmentation is performed in a \"surgically oriented\" mode, that is, the segmented area is normally slightly enlarged  
Figure 5: Original lesion with contours of the detected areas, segmented lesion adopting the implemented procedure, ground truth provided by the PH\({}^{2}\) database. with respect to the lesion area and the lesion contour is smoother than the pigmented lesion itself might appear. The implemented method is a versatile system which allows making changes in each section in an independent way. The presented method is designed to be sensitive even with images characterized by irregular borders. The method validation was limited to the only PH\({}^{2}\) database that, in any case, is a challenging dataset for the characteristics of images composing it. Future development will include the implementation of the feature extraction phase for lesion classification taking into account one among the adopted criteria for melanoma detection, such as the \"ABCDE rule\". In terms of the whole database images, the method achieved good overall performance, and in details the best specificity. Dice index and Jaccard coefficient were reached among the methods considered for the comparison.