Segmentation Of Pigmented Skin Lesions Research Articles

Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis.

The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies. The detection of melanoma in its early stage is essential for prognosis improvement and for guaranteeing a high five-year relative survival rate of patients. The clinical diagnosis of skin lesions is challenging and not trivial since it depends on human vision and physician experience and expertise. Therefore, a computer method that makes an accurate extraction of important details of skin lesion image can assist dermatologists in cancer detection. In particular, the border detection is a critical computer vision issue owing to the wide range of lesion shapes, sizes, colours and skin texture types. In this paper, an automatic and effective pigmented skin lesion segmentation method in dermoscopic image is presented. The proposed procedure is adopted to extract a mask of the lesion region without the adoption of other signal processing procedures for image improvement. A quantitative experimental evaluation has been performed on a publicly available database. The achieved results show the method validity and its high robustness towards irregular boundaries, smooth transition between lesion and skin, noise and artifact presence.

Sampling with level set for pigmented skin lesion segmentation

Melanoma is the deadliest form of skin cancer, and its incidence is increasing. The first step in automated melanoma analysis of dermoscopy images is to segment the area of the lesion from the surrounding skin. To improve the accuracy and adaptability of segmentation, an algorithm called sampling with level set by integrating color and texture (SLS-CT) is proposed that not only designs a new way to incorporate textural and color features in the definition of the energy functional but also utilizes an index called texture level, proposed in this work, to automatically decide the weight of each feature in the combined energies. First, at the preprocessing stage, hair and black frame removal is applied, and a potential lesion area is then obtained using Otsu thresholding and entropy maximization. Thereafter, the probability distribution of prior color in this potential lesion area is calculated as well. Second, Gabor wavelet-based texture features are extracted and integrated with the prior color into the evolving energies of the level set based on the texture level. To achieve global optimization, a Markov chain Monte Carlo sampling approach guided by the combined energy is adopted in evolving the level set, which ultimately defines a border in the image to segment a lesion from normal skin. Finally, morphological operations are used for postprocessing. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms, demonstrating that the proposed algorithm outperforms the other tested ones in terms of accuracy and adaptability to different databases.

A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images

This work proposes a simple and yet effective thresholding method to segment pigmented skin lesions in macroscopic photographs automatically. Segmentation is one of the first steps in computer-aided diagnosis of skin cancers. Therefore, an accurate segmentation may play an important clinical role. We develop an algorithm that searches for a thin rectangular-shaped region near the image borders that is likely to contain mostly skin pixels. Segmentation is obtained by adapting Otsu's thresholding method by combining independent threshold estimates computed from histograms of different parts of a new intensity image designed to discriminate lesions from background skin. The proposed approach exploits the fact that the object of interest is approximately centered in the input photograph. A cross-diagonal sampling scheme helps to balance the size of the classes when the area of the lesion and the area of the surrounding skin are very different. A post-processing stage that includes morphological filtering and a weighted scheme to select the most salient object follows. The experimental results suggest that the method potentially can be used successfully to segment atypical nevi and melanomas in lesions with a highly heterogeneous background skin. The proposed algorithm is of interest for use in clinical settings as part of a CAD system.

WITHDRAWN: Computational methods for the image segmentation of pigmented skin lesions: A Review

WITHDRAWN: Computational methods for the image segmentation of pigmented skin lesions: A Review

Automated Pre–processing Method for Dermoscopic Images and its Application to Pigmented Skin Lesion Segmentation

In this paper, we put forward a new pre–processing scheme for automatic analysis of dermoscopic images. Our contributions are two-fold. First, we present a procedure, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: these include shading induced by imaging non-flat skin surfaces and the effect of light-intensity falloff toward the edges of the dermoscopic image. This procedure is shown to facilitate the detection and removal of artifacts such as hairs as well. Second, we present a novel simple yet effective greyscale conversion approach that is based on physics and biology of human skin. Our proposed greyscale image provides high separability between a pigmented lesion and normal skin surrounding it. Finally, using our pre–processing scheme, we perform segmentation based on simple grey-level thresholding, with results outperforming the state of the art.

An incremental approach to pigmented skin lesion segmentation with classification refinements in uncertain regions.

Skin lesion segmentation in dermatoscopic images is difficult because there are large inter variations in shape, size, color, and texture between lesions and skin types. Hence, computational features learned from a training set of lesion images may not be applicable to other lesion images. In this paper, we propose an incremental method for lesion segmentation. It leverages the Expectation-Maximization algorithm to find an initial segmentation. A new adaptive method is proposed to define two types of segmented regions: the high-confident and the low-confident. We train a support vector machine, using computational features from the high-confident regions, to further refine segmentation and, hence, achieve improved results for the low-confident regions. Validation experiments of our proposed method are performed on 319 dermatoscopy images and we have achieved good results with precision and recall to be 0.864 and 0.875 respectively.

Watershed segmentation of dermoscopy images using a watershed technique

Automatic lesion segmentation is an important part of computer-based image analysis of pigmented skin lesions. In this research, a watershed algorithm is developed and investigated for adequacy of skin lesion segmentation in dermoscopy images. Hair, black border and vignette removal methods are introduced as preprocessing steps. The flooding variant of the watershed segmentation algorithm was implemented with novel features adapted to this domain. An outer bounding box, determined by a difference function derived from horizontal and vertical projection functions, is added to estimate the lesion area, and the lesion area error is reduced by a linear estimation function. As a post-processing step, a second-order B-Spline smoothing method is introduced to smooth the watershed border. Using the average of three sets of dermatologist-drawn borders as the ground truth, an overall error of 15.98% was obtained using the watershed technique. The implementation of the flooding variant of the watershed algorithm presented here allows satisfactory automatic segmentation of pigmented skin lesions.

Automated segmentation of pigmented skin lesions in multispectral imaging

The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions.