Unsupervised sub-segmentation for pigmented skin lesions
Machine Vision Laboratory, University of the West of England, Bristol, UK. Skin Research and Technology
(Impact Factor: 1.31).
05/2011; 18(1):77-87. DOI: 10.1111/j.1600-0846.2011.00534.x
Early identification of malignant melanoma with the surgical removal of thin lesions is the most effective treatment for skin cancers. A computer-aided diagnostic system assists to improve the diagnostic accuracy, where segmenting lesion from normal skin is usually considered as the first step. One of the challenges in the automated segmentation of skin lesions arises from the fact that darker areas within the lesion should be considered separate from the more general suspicious lesion as a whole, because these pigmented areas can provide significant additional diagnostic information.
This paper presents, for the first time, an unsupervised segmentation scheme to allow the isolation of normal skin, pigmented skin lesions, and interesting darker areas inside the lesion simultaneously. An adaptive mean-shift is first applied with a 5D spatial colour-texture feature space to generate a group of homogenous regions. Then the sub-segmentation maps are calculated by integrating maximal similarity-based region merging and the kernel k-means algorithm, where the number of segments is defined by a cluster validity measurement.
The proposed method has been validated extensively on both normal digital photographs and dermoscopy images, which demonstrates competitive performance in achieving automatic segmentation. The isolated dark areas have proved helpful in the discrimination of malignant melanomas from atypical benign nevi. Compared with the results obtained from the asymmetry measure of the entire lesion, the asymmetry distribution of the isolated dark areas helped increase the accuracy of the identification of malignant melanoma from 65.38% to 73.07%, and this classification accuracy reached 80.77% on integrating both asymmetry descriptors.
The proposed segmentation scheme gives the lesion boundary closed to the manual segmentation obtained by experienced dermatologists. The initial classification results indicate that the study of the distributions of darker areas inside the lesions is very promising in characterizing melanomas.
Figures in this publication
Available from: Alessia Amelio
- "A segmentation technique should trace the lesion borders as much accurately as possible and should avoid oversegmentation . Because of the importance of correct border identification, a high number of segmentation techniques have been developed using different approaches such as fuzzy logic based thresholding , clustering     , neural networks  , supervised learning , active contour , evolutionary computation . However, it is worth to note that there is no a general technique suitable for all the kinds of applications. "
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ABSTRACT: The development of computer-aided diagnosis systems for skin cancer detection has attracted a lot of interest in the research community. In particular, the availability of an accurate automatic segmentation tool for detecting skin lesions from background skin is of primary importance for the overall diagnosis system. In this paper we investigate the capability of a color image segmentation method based on Genetic Algorithms in discriminating skin lesions. Experimental results show that the segmentation approach is able to detect lesion borders quite accurately, thus coupled with a merging technique of the surrounding region could reveal a promising method for isolating skin tumor.
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation; 07/2013
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ABSTRACT: Computerised analysis on skin lesion images has been reported to be helpful in achieving objective and reproducible diagnosis of melanoma. In particular, asymmetry in shape, colour and structure reflects the irregular growth of melanin under the skin and is of great importance for diagnosing the malignancy of skin lesions. This paper proposes a novel asymmetry analysis based on a newly developed pigmentation elevation model and the global point signatures (GPSs). Specifically, the pigmentation elevation model was first constructed by computer-based analysis of dermoscopy images, for the identification of melanin and haemoglobin. Asymmetry of skin lesions was then assessed through quantifying distributions of the pigmentation elevation model using the GPSs, derived from a Laplace-Beltrami operator. This new approach allows quantifying the shape and pigmentation distributions of cutaneous lesions simultaneously. Algorithm performance was tested on 351 dermoscopy images, including 88 malignant melanomas and 263 benign naevi, employing a support vector machine (SVM) with tenfold cross-validation strategy. Competitive diagnostic results were achieved using the proposed asymmetry descriptor only, presenting 86.36 % sensitivity, 82.13 % specificity and overall 83.43 % accuracy, respectively. In addition, the proposed GPS-based asymmetry analysis enables working on dermoscopy images from different databases and is approved to be inherently robust to the external imaging variations. These advantages suggested that the proposed method has good potential for follow-up treatment.
Medical & Biological Engineering 03/2012; 50(5):503-13. DOI:10.1007/s11517-012-0895-7 · 1.73 Impact Factor
Available from: Rafael Garcia
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Computerized analysis of pigmented skin lesions (PSLs) is an active area of research that dates back over 25years. One of its main goals is to develop reliable automatic instruments for recognizing skin cancer from images acquired in vivo. This paper presents a review of this research applied to microscopic (dermoscopic) and macroscopic (clinical) images of PSLs. The review aims to: (1) provide an extensive introduction to and clarify ambiguities in the terminology used in the literature and (2) categorize and group together relevant references so as to simplify literature searches on a specific sub-topic.
Methods and material:
The existing literature was classified according to the nature of publication (clinical or computer vision articles) and differentiating between individual and multiple PSL image analysis. We also emphasize the importance of the difference in content between dermoscopic and clinical images.
Various approaches for implementing PSL computer-aided diagnosis systems and their standard workflow components are reviewed and summary tables provided. An extended categorization of PSL feature descriptors is also proposed, associating them with the specific methods for diagnosing melanoma, separating images of the two modalities and discriminating references according to our classification of the literature.
There is a large discrepancy in the number of articles published on individual and multiple PSL image analysis and a scarcity of reported material on the automation of lesion change detection. At present, computer-aided diagnosis systems based on individual PSL image analysis cannot yet be used to provide the best diagnostic results. Furthermore, the absence of benchmark datasets for standardized algorithm evaluation is a barrier to a more dynamic development of this research area.
Artificial intelligence in medicine 10/2012; 56(2). DOI:10.1016/j.artmed.2012.08.002 · 2.02 Impact Factor
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