Publications

An automated algorithm is presented to determine fragment DNA size from Atomic Force Microscope images. Several real and synthetic images were tested for different image and fragment sizes and different background noises. The automated approach allows to minimize processing time with respect to manual DNA sizing and to extract information that can be used to perform further analysis on the molecules. For computer-generated test images the percentage error in length estimation is less than 1% and its average value is 0.4%. For real images the deviation with respect to manually-performed length estimation is around 1%.

In this paper we define a Topological Tree (TT) as a knowledge representation method that aims to describe important visual and spatial features of image regions, namely the color similarity, the inclusion and the spatial adjacency. The topological tree exhibits some interesting properties that can be exploited to extract knowledge from images for information retrieval, image understanding and diagnosis purposes. Examples of applications in dermatology are described. 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 Principal Component Analysis of the color space. The recursive FCM proves to be effective for underlining the adjacency and inclusion property of regions.

Clinical evaluation of pigmented skin lesion images is subjective and can lead to different results depending on the examiner’s experience, also applying semiquantitative methods such as the ABCD rule for dermatoscopy. In order to increase the reproducibility of clinical judgement, a method to automatically reproduce the A (Asymmetry) and the B (Border) parameters of the ABCD rule was developed. One hundred and fourteen images of melanomas acquired by a digital videomicroscope with a 20x magnification were studied.Clinical evaluation: a clinical judgement of asymmetry of the shape and pigment distribution along 2 axes were performed by 0–2 scoring system. For the evaluation of the border cut-off, a score ranging to 0 from 8 was attributed to each lesion on the basis of the number of segments with an abrupt edge interruption of the pigmentation. Computer elaboration: after automatic border detection, major and minor axes were obtained and ‘shape asymmetry’ on each axis was calculated considering the proportion of overlapping pixels. A correspondence lower than 90% was selected as the threshold for asymmetry. The ‘pigment distribution asymmetry’ on each axis was calculated comparing the portion of the dark area, obtained by the median cut algorithm, in the two halves of the lesion. A correspondence lower than 80% was considered as the threshold for asymmetry. In order to numerically describe the gradient at the border, the lesion border was divided into 8 segments and the change in lightness values along a 30 pixel long segment centered on the lesion border, expressed as the slope of the curve, was considered. Threshold for abrupt border cut-off was set by a slope greater than 3.609Results: a good correlation between clinical evaluation and computer elaboration was found for shape asymmetry (rho=0.698;p<0.001), pigment distribution asymmetry (rho=0.428;p<0.001) and number of borders with an abrupt cut-off (rho=0.834;p<0.001).

This work presents a general-purpose method for moving visual object segmentation in videos and discusses results attained on sequences of PETS2002 datasets. The proposed approach, called Sakbot, exploits color and motion information to detect objects, shadows and ghosts, i.e. foreground objects with apparent motion. The method is based on background suppression in the color space. The main peculiarity of the approach is the exploitation of motion and shadow information to selectively update the background, improving the statistical background model with the knowledge of detected objects. The approach is able to detect Moving Visual Objects (MVOs), and stopped objects too, since the motion status is maintained at the level of tracking module. HSV color space is exploited for shadow detection in order to enhance both segmentation and background update. Time measures and precision performance analysis in tracking and counting people is provided for surveillance and monitoring purposes.

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.