Melanocytic lesions of acral sites (ALM) are common, with an estimated prevalence of 28 - 36% in the USA. While the majority of these lesions are benign, differentiation from acral melanoma (AM) is often challenging. Much research has been done in segmenting and classifying skin moles located in acral volar areas. However, methods published to date cannot be easily extended to new skin regions because of different appearance and properties. In this paper, we propose a deep learning (U-Net) architecture to segment acral melonacytic lesions which is a necessary initial step for skin lesion pattern recognition, furthermore it is a prerequisite step to provide an accurate classification and diagnosis. The U-Net is one of the most promising deep learning solution for image segmentation and is built upon fully convolutional network. On the independent validation dataset including 210 dermoscopy images our implemented method showed high segmentation accuracy. For the U-Net convolutional neural network, an average DSC of 0.92, accuracy 0.94, sensitivity 0.91, and specificity 0.92 has been achieved. ALM due to small size and similarity to other local structures create enormous difficulties during the segmentation and assessment process. The use of advanced segmentation methods like deep learning models especially convolutional neural networks have the potential to improve the accuracy of advanced medical area segmentation.
The black ink (India ink) and melanin are substances often found in histopathological images of skin specimens. If present in abundant quantity, they negatively affect the outcome of automatic stain deconvolution methods. We propose an automatic black ink and melanin segmentation method based on global color threshol- ding in CIELAB color space combined with a novel region growing approach. Our technique achieved sensitivity of 87 %, specificity of 99 %, and precision of 94% for black ink detection. It segmented melanin with sensitivity of 93 %, specificity of 99 %, and precision of 84 %. When excluding certain regions of images before performing color deconvolution, we observed better approximation of the stain unmixing matrix.
Background: Epidermis area is an important observation area for the diagnosis of inflammatory skin diseases
and skin cancers. Therefore, in order to develop a computer-aided diagnosis system, segmentation of the epidermis
area is usually an essential, initial step. This study presents an automated and robust method for epidermis
segmentation in whole slide histopathological images of human skin, stained with hematoxylin and eosin.
Methods: The proposed method performs epidermis segmentation based on the information about shape
and distribution of transparent regions in a slide image and information about distribution and concentration of
hematoxylin and eosin stains. It utilizes domain-specific knowledge of morphometric and biochemical properties
of skin tissue elements to segment the relevant histopathological structures in human skin.
Results: Experimental results on 88 skin histopathological images from three different sources show that
the proposed method segments the epidermis with a mean sensitivity of 87 %, a mean specificity of 95% and a
mean precision of 57%. It is robust to inter- and intra-image variations in both staining and illumination, and
makes no assumptions about the type of skin disorder. The proposed method provides a superior performance
compared to the existing techniques.