Publications

Although an improvement of the diagnostic accuracy of pigmented skin lesions (PSL) has been achieved by the epiluminescence technique (ELM), the interpretation of ELM criteria is often confusing, especially for inexperienced observers. To enhance the reproducibility and accuracy of clinical judgement and the training of inexperienced operators, programs for PSL image analysis and algorithms for automatic diagnosis have been developed. The aim of our study was to develop a new program for PSL image analysis, able to describe different aspects of PSLs and to test its descriptive capability on PSL acquired by means of a digital videomicroscope (VMS 110A, Scalar Mitsubishi, Japan) using 20-fold magnification. After automatic border identification and baricentre determination, some geometric parameters, describing shape characteristics of the lesion, were calculated. A mathematical description of the border cut-off was obtained. The texture of the lesion was calculated applying the co-occurrence matrix at different image resolutions. Dark areas and colour areas, referring to selected colour groups, were obtained and their aspect and distribution were mathematically defined and calculated. 281 common nevi and 117 melanomas were numerically described by our program and the capability of the mathematical parameters to distinguish between benign and malignant lesion was tested by means of discriminant analysis. Significant differences were observed for most parameters between different PSL populations. The automatic classification enabled the distinction between melanomas and nevi with a 100% sensitivity and a 82.9% specificity.

In this paper we define a novel approach for image segmentation into regions which focuses on both visual and topological cues, namely color similarity, inclusion and spatial adjacency. Many color clustering algorithms have been proposed in the past for skin lesion images but none exploits explicitly the inclusion properties between regions. Our algorithm is based on a recursive version of fuzzy c-means (FCM) clustering algorithm in the 2D color histogram constructed by Principal Component Analysis (PCA) of the color space. The distinctive feature of the proposal is that recursion is guided by the evaluation of adjacency and mutual inclusion properties of extracted regions; then, the recursive analysis addresses only included regions or regions with a not-negligible size. This approach allows a coarse-to-fine segmentation which focuses the attention on the inner parts of the images, in order to highlight the internal structure of the object depicted in the image. This could be particularly useful in many applications, especially in the biomedical image analysis. In this work we apply the technique to the segmentation of skin lesions in dermatoscopic images. It could be a suitable support for the diagnosis of skin melanoma, since dermatologists are interested in the analysis of the spatial relations, the symmetrical positions and the inclusion of regions.

Image analysis tools are spreading in dermatology since the introduction of dermoscopy (epiluminescence microscopy), in the effort of algorithmically reproducing clinical evaluations. Color-based region segmentation of skin lesions is one of the key steps for correctly collecting statistics that can help clinicians in their diagnosis. Nevertheless, an efficient and accurate region segmentation algorithm has not been proposed in the literatureyet. This work proposes an iterative fuzzy c-means clustering algorithm based on PCA with the Karhunen-Loève transform of the color space. A topological tree is provided to store the mutual inclusions of the regions and then used to summarize the structural properties of the skin lesion. Preliminary experimental results are presented and discussed.

In this paper we present transcoding techniques for a video server architecture that enables the user to access live video streams by using different devices with different capabilities. For live videos, annotation methods cannot be exploited. Instead we propose methods of on-the-fly transcoding that adapt the video content with respect to the user resources and the video semantic. Thus we propose an object-based transcoding with "classes of relevance" (for instance People, Face and Background). To compare the different strategies we propose a metric based on the Weighted Mean Square Error that allows the analysis of different application scenarios by means of a class-wise distortion measure. The obtained results show that the use of semantic can improve the bandwidth to distortion ratio significantly.

In this paper we present transcoding techniques for a video server architecture that enables the user to access live video streams by using different devices with different capabilities. For live videos, annotation methods cannot be exploited. Instead we propose methods of on-the-fly transcoding that adapt the video content with respect to the user resources and the video semantic. Thus we propose an object-based transcoding with "classes of relevance"(for instance People, Face and Background). To compare the different strategies we propose a metric based on the Weighted Mean Square Error that allows the analysis of different application scenarios by means of a class-wise distortion measure. The obtained results show that the use of semantic can improve the bandwidth to distortion ratio significantly.

In this work we describe the Topological Tree (TT) as a knowledge representation method that relates some important visual and spatial features of image regions, namely the color similarity, the inclusion and the spatial adjacency. Starting from color-based region segmentation of an image into disjoint regions, their spatial relationships can be devised and described with graph-based methods. We are interested in the region’s propriety “to be included into” (in the sense of “surrounded by”) another region. This property could be very useful in biomedical imaging and in particular in the diagnosis of skin melanoma. The TT can be constructed after segmentation, by computing the spatial relationships of regions or can be generated directly during the segmentation: to this aim we present a novel recursive fuzzy c-means (FCM) clustering algorithm based on the PCA of the color space. In the paper, in addition to the TT definition and the construction algorithm description, some results are presented and discussed.

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Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a general-purpose method for segmentation of moving visual objects (MVOs) based on an object-level classification in MVOs, ghosts and shadows. Background suppression needs a background model to be estimated and updated: we use motion and shadow information to selectively exclude from the background model MVOs and their shadows, while retaining ghosts. The color information (in the HSV color space) is exploited to shadow suppression and, consequently, to enhance both MVOs segmentation and background update.