Digital dermoscopy in clinical practise: a three‐centre analysis

Wollina, Uwe; Burroni, Marco; Torricelli, Rocco; Gilardi, S.; Dell'Eva, Giordana; Helm, Cathrine; Bardey, Wolfgang

doi:10.1111/j.1600-0846.2007.00219.x

Cited by 58 publications

(52 citation statements)

References 29 publications

(41 reference statements)

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“…Several automatic image-based diagnosis systems have been developed by research groups involved in biomedical image analysis in past years (Dorileo et al, 2008;Rahman et al, 2006;Schmid-Saugeons et al, 2003;Wollina et al, 2007).…”

Section: Dermoscopic Cbir: the Fide Systemmentioning

confidence: 99%

Automated content based image retrieval technology (CBIR) and ITS application on dermoscopic images of pigmented skin lesions

Baldi

Bizzi

2015

MRAJ

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Dermoscopy (dermatoscopy, epiluminescence microscopy) is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs). This diagnostic tool permits the recognition of morphological structures not visible to the naked eye, thereby providing a new dimension to the analysis of the clinical morphologic features of PSLs. Due to the difficulty and variability of human interpretation, computerized analysis of dermoscopy images has become an important area of research and Numerous systems designed to provide computer-aided analysis of digital images have recently been reported in literature. Contentbased image retrieval systems (CBIR) technology exploits the visual content in image data. It proposes to benefit the management of increasingly large biomedical image collections as well as to aid clinical medicine, research and education. The goal of this article is to describe the application of CBIR systems for dermoscopic images, describing in detail the FIDE model created in our laboratory. FIDE's function is to document the image analysis side of the diagnostic process, focusing on accompanying and aiding it, and on providing efficient digital atlas navigation by providing precision (cases most similar to the one under analysis) and clarifying context (similar cases in different categories). The FIDE system is effective in retrieving PSL images with known pathology visually similar to the image under evaluation, giving a valuable and intuitive aid to the clinician in the decision making process. FIDE represents, indeed, the first CBIR system successfully applied to dermoscopic images. The real impact of this system in clinical practice will be revealed only by its application in a wide clinical setting.

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Section: Dermoscopic Cbir: the Fide Systemmentioning

confidence: 99%

Automated content based image retrieval technology (CBIR) and ITS application on dermoscopic images of pigmented skin lesions

Baldi

Bizzi

2015

MRAJ

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“…Basically, the key to attaining a successful retrieval system is to choose the right features that represent each class of images as uniquely as possible. Many feature extraction strategies have been proposed [6,7] from the perspective of classification of images as malignant or benign. Different features attempt to reflect the parameters used in medical diagnosis, such as the ABCD rule for melanoma detection [18].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Concerning segmentation, Celebi et al [5] presented a systematic overview of recent border detection methods: clustering followed by active contours are the most popular. Numerous features have been extracted from skin images, including shape, colour, texture and border properties [6][7][8]. Classification methods range from discriminant analysis to neural networks and support vector machines [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis

Ballerini

Fisher

et al. 2010

Applications of Evolutionary Computation

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Abstract. This paper gives an example of evolved features that improve image retrieval performance. A content-based image retrieval system for skin lesion images is presented. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types, are used. Colour and texture features are extracted from lesions. Evolutionary algorithms are used to create composite features that optimise a similarity matching function. Experiments on our database of 533 images are performed and results are compared to those obtained using simple features. The use of the evolved composite features improves the precision by about 7%.

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“…Numerous features have been extracted from skin images, including shape, colour, texture and border properties. 6,7,8 Classification methods range from discriminant analysis to neural networks and support vector machines.…”

Section: Some Authorsmentioning

confidence: 99%

Fuzzy description of skin lesions

Laskaris

Ballerini

Fisher

et al. 2010

Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment

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We propose a system for describing skin lesions images based on a human perception model. Pigmented skin lesions including melanoma and other types of skin cancer as well as non-malignant lesions are used. Works on classification of skin lesions already exist but they mainly concentrate on melanoma. The novelty of our work is that our system gives to skin lesion images a semantic label in a manner similar to humans. This work consists of two parts: first we capture they way users perceive each lesion, second we train a machine learning system that simulates how people describe images. For the first part, we choose 5 attributes: colour (light to dark), colour uniformity (uniform to non-uniform), symmetry (symmetric to non-symmetric), border (regular to irregular), texture (smooth to rough). Using a web based form we asked people to pick a value of each attribute for each lesion. In the second part, we extract 93 features from each lesions and we trained a machine learning algorithm using such features as input and the values of the human attributes as output. Results are quite promising, especially for the colour related attributes, where our system classifies over 80% of the lesions into the same semantic classes as humans.

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Digital dermoscopy in clinical practise: a three‐centre analysis

Cited by 58 publications

References 29 publications

Automated content based image retrieval technology (CBIR) and ITS application on dermoscopic images of pigmented skin lesions

Automated content based image retrieval technology (CBIR) and ITS application on dermoscopic images of pigmented skin lesions

Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis

Fuzzy description of skin lesions

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