Publications

The assessment of asymmetry is essential for melanoma (MM) diagnosis, both when using a heuristic approach and when employing semiquantitative methods on dermoscopic images. The aim of our study was to develop and validate a software for assessment of asymmetry in melanocytic lesion images, based on evaluation of color symmetry, and to compare the automatic evaluation to the one performed by human observers. An image analysis program enabling the numerical assessment of asymmetry in melanocytic lesions, based on evaluation and comparison of RGB color components inside image color blocks, was employed on 459 videomicroscopic digital images, referring to 95 melanomas (MMs), 76 atypical nevi (AN) and 288 clearly benign nevi (BN). Clinical evaluation of asymmetry on dermoscopic images was performed on the same image set employing a 0–1 scoring system. Asymmetry judgement was expressed by the clinicians for 12.8% of BN, 44.7% of AN, and for 64.2% of MMs, whereas the computer identified 6.9% of BN, 27.6% of AN, and 87.4% of MMs as asymmetric. Sensitivity and specificity of clinical judgement were 64.2 and 80.5%, respectively, whereas for computer evaluation, a sensitivity of 87.5% and a specificity of 85.7% were obtained. Numerical parameters (mean RGB distance, variance and maximum distance) referring to MMs were significantly higher both with respect to BN and AN. This innovative method for automatic asymmetry evaluation, based on the mathematical description of color distribution in different image blocks, provides numerical parameters for employment in computer-aided melanoma diagnosis.

In this paper we address the problem of human posture classification, in particular focusing to an indoor surveillance application. The approach was initially inspired to a previous works of Haritaoglou et al. [5] that uses histogram projections to classify people’s posture. Projection histograms are here exploited as the main feature for the posture classification, but, differently from [5], we propose a supervised statistical learning phase to create probability maps adopted as posture templates. Moreover, camera calibration and homography are included to solve perspective problems and to improve the precision of the classification. Furthermore, we make use of a finite state machine to detect dangerous situations as falls and to activate a suitable alarm generator. The system works on-line on standard workstations with network cameras.

The aim of this study was to develop a computerized method for the identification and description of colour areas in melanocytic lesion images based on an approach mimicking the human perception of colours. A colour palette comprising six colour groups (black, dark brown, light brown, blue-grey, red and white) was created by selecting single colour components within melanocytic lesion images acquired using a digital videomicroscope, and was implemented in the image analysis program. For each colour region, the area, the distance from the lesion centroid, the spread, the colour area distribution in the internal and the external part of the lesion, and asymmetries were assessed on 604 melanocytic lesion images in our image database. Black, white and blue-grey colour areas were detected more frequently in melanomas compared with naevi. Moreover, significant differences in colour descriptors were observed for each colour group, showing that colour areas are more unevenly distributed in melanomas compared with naevi. Using a discriminant analysis approach, the extension of dark, white and blue-grey areas and some descriptors of the distribution of the colour areas were identified as the most relevant colour parameters for differentiating between benign and malignant lesions. In conclusion, our automatic procedure breaks down the image into the colour areas used in the clinical examination process, and also supplies a description of their extension and distribution, with parameters that correlate with the clinical concepts of regularity and homogeneity.

Background: Identification of dark areas inside a melanocytic lesion (ML) is of great importance for melanoma diagnosis, both during clinical examination and employing programs for automated image analysis. Objective: The aim of our study was to compare two different methods for the automated identification and description of dark areas in epiluminescence microscopy images of MLs and to evaluate their diagnostic capability. Methods: Two methods for the automated extraction of ´absolute´ (ADAs) and ´relative´ dark areas (RDAs) and a set of parameters for their description were developed and tested on 339 images of MLs acquired by means of a polarized-light videomicroscope. Results: Significant differences in dark area distribution between melanomas and nevi were observed employing both methods, permitting a good discrimination of MLs (diagnostic accuracy = 74.6 and 71.2% for ADAs and RDAs, respectively). Conclusions: Both methods for the automated identification of dark areas are useful for melanoma diagnosis and can be implemented in programs for image analysis. Copyright

In this work, we describe a technique to calibrate images for skin analysis in dermatology. Using a common reference we correct non-uniform illumination effects, give an estimation of the gamma correction and produce a XYZ conversion matrix. The final result is then reverted to a non standard RGB color space, built from the instrument images. In this way different instruments behave uniformly allowing colorimetric characterization, while improving the results of common algorithms. The proposed techniques should be the initial support for a distributed framework where dermatological images can be consistently compared.

Atypical nevi share some dermoscopic features with early melanoma, and computer elaboration of digital images could represent a useful support to diagnosis. The aim of our study was to automatically assess colors in atypical nevi, and to compare the data with those referring to clearly benign nevi and melanomas. Dermoscopic images of 459 melanocytic lesions, referring to 76 atypical nevi, 288 clearly benign nevi and 95 melanomas, were acquired by means of a digital videomicroscope (Videocap 100, DS-Medica, Italy) employing a 20-fold magnification. An image analysis program, based on an approach, which shares some similarities with the human perception of colors, was employed. For the evaluation of colors in melanocytic lesion images, the identification of the six main color groups (black, dark brown, light brown, red, white and blue-gray) and the numerical description of color areas were obtained. Black, white and blue-gray were more frequently found in atypical nevi than in clearly benign nevi, but less frequently than in melanomas. Color area values significantly differed between the three groups, showing increasing irregularity in color distribution from benign lesions to atypical nevi and melanomas. The clinical–morphological interpretation of the numerical data, based on the mathematical description of the aspect and distribution of different color areas in different lesion types may contribute to the characterization of atypical nevi and their distinction from melanomas.

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Spitz nevi may be often confused with malignant melanoma, because of its rapid growth and alarming clinical features. In vivo confocal reflectance microscopy (CRM) is a novel technique enabling the noninvasive imaging of the skin at a cellular level resolution. Twelve Spitz and 25 melanomas (MMs) were studied by means of CRM (Vivascope 1000, Lucid Inc., USA) and digital dermoscopy (Videocap 200, DS-Mediroup, Italy) for in vivo characterization of cytological and architectural features at CRM, and correlation with dermoscopy and histology. Although large cells with bright cytoplasm and dark eccentric nucleus, sometimes spreading upwards in a pagetoid fashion, were observed both in Spitz nevi and MMs, in the latter case they were more numerous and irregularly shaped. Dermoscopic globules corresponded to cell clusters at CRM and melanocytic nests at histopathology. Spitz nevi frequently presented a peripheral rim of medium sized peripheral clusters, constituted by compact aggregates of large polygonal cells, sometimes observable also on the whole lesion area. In MMs cell clusters were frequently constituted by sparse cells intercalated with thin fibrils giving a multi-lobate appearance or by large confluent aggregates of low reflecting polygonal or elongated cells, resulting in a cerebriform appearance. Although CRM appeared useful for distinction between melanocytic lesions, Spitz nevi presenting numerous atypical cells and dermal-epidermal architecture disarrangement can not be always distinguished from MMs, owing to the limited penetration of the near-infrared laser light, not enabling the evaluation of ‘cell maturation’ with increasing depth.