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Marco Burroni

Publications and source records attributed to Marco Burroni.

8 recordsLinked to original sources
PubMed2006

Comments on "A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions".

In this paper, discrepancies and reference inaccuracies in the paper (Grana et al., 2003) are pointed out. Specifically, it is demonstrated that the definitions of "lesion gradient" and "skin lesion gradient," widely used in a number of medical papers on computer analysis of pigmented skin lesions, are unambiguous, and that the "new algorithm for border description" described in the subject paper substantially relies on well-established concepts dating back over one decade ago.

Algorithms↗
PubMed2004

Melanoma computer-aided diagnosis: reliability and feasibility study.

BACKGROUND: Differential diagnosis of melanoma from melanocytic nevi is often not straightforward. Thus, a growing interest has developed in the last decade in the automated analysis of digitized images obtained by epiluminescence microscopy techniques to assist clinicians in differentiating early melanoma from benign skin lesions. PURPOSE: The aim of this study was to evaluate diagnostic accuracy provided by different statistical classifiers on a large set of pigmented skin lesions grabbed by four digital analyzers located in two different dermatological units. EXPERIMENTAL DESIGN: Images of 391 melanomas and 449 melanocytic nevi were included in the study. A linear classifier was built by using the method of receiver operating characteristic curves to identify a threshold value for a fixed sensitivity of 95%. A K-nearest-neighbor classifier, a nonparametric method of pattern recognition, was constructed using all available image features and trained for a sensitivity of 98% on a large exemplar set of lesions. RESULTS: On independent test sets of lesions, the linear classifier and the K-nearest-neighbor classifier produced a mean sensitivity of 95% and 98% and a mean specificity of 78% and of 79%, respectively. CONCLUSIONS: In conclusion, our study suggests that computer-aided differentiation of melanoma from benign pigmented lesions obtained with DB-Mips is feasible and, above all, reliable. In fact, the same instrumentations used in different units provided similar diagnostic accuracy. Whether this would improve early diagnosis of melanoma and/or reducing unnecessary surgery needs to be demonstrated by a randomized clinical trial.

Diagnosis, Computer-Assisted↗
PubMed2004

Digital surface microscopy analysis of conjunctival pigmented lesions: a preliminary study.

The objective of this study was to investigate whether digital surface microscopy (DSM) could be used for the follow-up and comparison of malignant and benign conjunctival pigmented lesions (CPLs). Thirty-nine CPLs [16 de novo malignant melanomas (MMs), one MM arising from primary acquired melanosis (PAM), six PAMs and 16 naevi] were digitally analysed and biopsied. All of the PAMs and 10 naevi, which had not been surgically excised, were followed up using DSM. Thirty parameters were evaluated grouped into four categories: geometry, colour, texture and islands of colour. None of the CPLs that were followed up, which comprised 10 naevocytic naevi and seven PAMs, showed any morphological change at DSM analysis, except for one PAM which developed an MM 1 year later. Of the geometric variables examined, the area, maximum diameter and minimum diameter showed significantly higher values in MMs compared with benign CPLs. With regard to the colour of CPLs, MMs were significantly darker and bluer than naevi. In the texture group, contrast was significantly higher in MMs. In the islands-of-colour group, the imbalance of blue-grey regions and the presence of dark areas were significantly higher in MMs. DSM greatly simplified the follow-up of CPLs, such as PAMs with atypia, by providing satisfactory quality images with high reproducibility; this technique is also easy to use and well accepted by patients. Moreover, this preliminary study allowed us to determine which objective variables could be important for distinguishing between benign CPLs and conjunctival MMs.

Adolescent↗
PubMed2004

Inter- and intra-variability of pigmented skin lesions: could the ABCD rule be influenced by host characteristics?

BACKGROUND/PURPOSE: Many differences in color, shape and dimension exist between different moles even in the same individual. Major differences might be accounted for anatomical location, genetic factors and by environmental factors, mainly sunlight exposure. Therefore, it would be of great value, when evaluating skin lesions, to take into account the degree of intra- and inter-variability of several diagnostic parameters. In order to assess the morphologic and chromatic differences between lesions belonging to different patients and between lesions belonging to the same individual, we examined objective digital parameters obtained with dermatoscopic analysis, using the DBDermo MIPS system (BIO MIPS Engineering, S.R.L, siena, Italy). METHODS: The automatic classifier inside the software is based on a 'match by similarity' algorithm, based on the measurement of the Euclidean distances of all variables considered from the reference image. Two-hundred and four clinically benign pigmented lesion, belonging to 18 patients were examined, stored and automatically processed. For each lesion objective parameters related to geometry, color and texture were automatically evaluated. RESULTS: We found skin color (healthy skin) is objectively different from subject to subject and the lesion color is more similar among different lesions of the same patient than among lesions belonging to different individuals both in their darkest and slightly dark component. We also observed that lesion dimensions are individual correlates, i.e. the probability for a lesion to be large is higher when the other, in the same patient, is large. CONCLUSION: Many parameters of pigmented skin lesions evaluated by digital dermoscopy analysis are similar in the same patient and different from those belonging to different individuals. This indicates that, when considering a lesion, we should take into account the peculiar patient's characteristics.

Diagnosis, Computer-Assisted↗
PubMed2002

Automated diagnosis of pigmented skin lesions.

Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions.

Automation↗
PubMed2002

Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study.

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.

Humans↗