Publications

In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, resulting in a lack of comparative evaluation studies on a common benchmark. To address this scientific gap and encourage deep learning research in the field, the ToothFairy challenge was organized within the MICCAI 2023 conference. In this context, a public dataset was released to also serve as a benchmark for future research. The dataset comprises 443 CBCT scans, with voxel-level annotations of the IAC available for 153 of them, making it the largest publicly available dataset of its kind. The participants of the challenge were tasked with developing an algorithm to accurately identify the IAC using the 2D and 3D-annotated scans. This paper presents the details of the challenge and the contributions made by the most promising methods proposed by the participants. It represents the first comprehensive comparative evaluation of IAC segmentation methods on a common benchmark dataset, providing insights into the current state-of-the-art algorithms and outlining future research directions. Furthermore, to ensure reproducibility and promote future developments, an open-source repository that collects the implementations of the best submissions was released.

In this paper we present a segmentation proposal method which employs a box-hypotheses generation step followed by a lightweight segmentation strategy. Inspired by interactive segmentation, for each automatically placed bounding-box we compute a precise segmentation mask. We introduce diversity in segmentation strategies enhancing a generic model performance exploiting class-independent regional appearance features. Foreground probability scores are learned from groups of objects with peculiar characteristics to specialize segmentation models. We demonstrate results comparable to the state-of-the-art on PASCAL VOC 2012 and a further improvement by merging our proposals with those of a recent solution. The ability to generalize to unseen object categories is demonstrated on Microsoft COCO 2014.

In this paper, the problem of automatic people removal from digital photographs is addressed. Removing unintended people from a scene can be very useful to focus further steps of image analysis only on the object of interest, A supervised segmentation algorithm is presented and tested in several scenarios.

In this paper a system for illuminated manuscripts images analysis is presented. In particular the bag-of-keypoints strategy, commonly adopted for object recognition, image classification and scene recognition, is applied to the classification of automatically extracted miniatures. Pictures are characterized by SURF descriptors, and a classification procedure is performed, comparing the results of Naive Bayes and histogram intersection distance measures.

In this paper we detail a proposal of exploitation of expert-made commentaries in a unified system for illuminated manuscripts images analysis. In particular we will explore the possibility to improve the automatic segmentation of meaningful pictures, as well as the retrieval by similarity search engine, using clusters of keywords extracted from commentaries as semantic information.

In this paper a document analysis tool for historical manuscripts is proposed. The goal is to automatically segment layout components of the page, that is text, pictures and decorations. We specifically focused on the pictures, proposing a set of visual features able to identify significant pictures and separating them from all the floral and abstract decorations. The analysis is performed by blocks using a limited set of color and texture features, including a new texture descriptor particularly effective for this task, namely Gradient Spatial Dependency Matrix. The feature vectors are processed by an embedding procedure which allows increased performance in later SVM classification.

In this work we propose a system for automatic document segmentation to extract graphical elements from historical manuscripts and then to identify significant pictures from them, removing floral and abstract decorations. The system performs a block based analysis by means of color and texture features. The Gradient Spatial Dependency Matrix, a new texture operator particularly effective for this task, is proposed. The feature vectors are processed by an embedding procedure which allows increased performance in later SVM classification. Results for both feature extraction and embedding based classification are reported, supporting the effectiveness of the proposal.

In this paper we present a system for automatic segmentation, annotation and image retrieval based on content, focused on illuminated manuscripts and in particular the Borso D'Este Holy Bible. To enhance the interaction possibilities with this work, full of decorations and illustrations, we exploit some well known document analysis techniques in addition to some new approaches, in order to achieve good segmentation of pages into meaningful visual objects with the relative annotation. We wanted to extend the standard keyword-based retrieval approach in a commentary with a modern visual-based retrieval by appearance similarity: an entire software user interface for exploration and visual search of illuminated manuscripts.

In this paper we focus on the problem of automatic classification of melanocytic lesions, aiming at identifying the presence of reticular patterns. The recognition of reticular lesions is an important step in the description of the pigmented network, in order to obtain meaningful diagnostic information. Parameters like color, size or symmetry could benefit from the knowledge of having a reticular or non-reticular lesion. The detection of network patterns is performed with a three-steps procedure. The first step is the localization of line points, by means of the line points detection algorithm, firstly described by Steger. The second step is the linking of such points into a line considering the direction of the line at its endpoints and the number of line points connected to these. Finally a third step discards the meshes which couldn't be closed at the end of the linking procedure and the ones characterized by anomalous values of area or circularity. The number of the valid meshes left and their area with respect to the whole area of the lesion are the inputs of a discriminant function which classifies the lesions into reticular and non-reticular. This approach was tested on two balanced (both sets are formed by 50 reticular and 50 non-reticular images) training and testing sets. We obtained above 86% correct classification of the reticular and non-reticular lesions on real skin images, with a specificity value never lower than 92%.

Automatic segmentation of skin lesions in clinical images is a very challenging task; it is necessary for visual analysis of the edges, shape and colors of the lesions to support the melanoma diagnosis, but, at the same time, it is cumbersome since lesions (both naevi and melanomas) do not have regular shape, uniform color, or univocal structure. Most of the approaches adopt unsupervised color clustering. This works compares the most spread color clustering algorithms, namely median cut, k-means, fuzzy-c means and mean shift applied to a method for automatic border extraction, providing an evaluation of the upper bound in accuracy that can be reached with these approaches. Different tests have been performed to examine the influence of the choice of the parameter settings with respect to the performances of the algorithms. Then a new supervised learning phase is proposed to select the best number of clusters and to segment the lesion automatically. Examples have been carried out in a large database of medical images, manually segmented by dermatologists. From these experiments mean shift was resulted the best technique, in term of sensitivity and specificity. Finally, a qualitative evaluation of the goodness of segmentation has been validated by the human experts too, confirming the results of the quantitative comparison.