Publications by Costantino Grana

Background Semiquantitative algorithms were applied to dermoscopic images to improve the clinical diagnosis for melanoma. Objective The aim of the study was to develop a computerized method for automated quantification of the 'A' (asymmetry) and 'B' (border cut-off) parameters, according to the ABCD rule for dermoscopy, thus reproducing human evaluation. Methods Three hundred and thirty-one melanocytic lesion images, referring to 113 melanomas and 218 melanocytic nevi, acquired by means of a digital videodermatoscope, were considered. Images were evaluated by two experienced observers and by using computer algorithms developed by us. Clinical evaluation of asymmetry was performed by attributing scores to shape asymmetry and asymmetry of pigment distribution and structures, whereas computer evaluation of shape and pigment distribution asymmetries were based on the assessment of differences in area and lightness in the two halves of the image, respectively. Borders were evaluated both by clinicians and by the computer, by attributing a score to each border segment ending abruptly. Differences between nevus and melanoma values were evaluated using the chi-square test, while Cohen's Kappa index for agreement was employed for the evaluation of the concordance between human and computer. Results Pigment distribution asymmetry appears the most striking parameter for melanoma diagnosis both for human and for automated diagnosis. A good concordance between clinicians and computer evaluation was achieved for all asymmetry parameters, and was excellent for border cut-off evaluation. Conclusions These algorithms enable a good reproduction of the 'A' and 'B' parameters of the ABCD rule for dermoscopy, and appear useful for diagnostic and learning purposes.

Digital dermoscopy improves the accuracy of melanoma diagnosis. The aim of this study was to develop and validate software for assessment of asymmetry in melanocytic lesion images, based on evaluation of colour symmetry, and to compare it with assessment by human observers. An image analysis program enabling numerical assessment of asymmetry in melanocytic lesions, based on the evaluation and comparison of CIE L*a*b* colour components (CIE L*a*b* is the name of a colour space defined by the Commission Internationale de l'Eclairage) inside image colour blocks, was employed on the recorded lesion images. Clinical evaluation of asymmetry in 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 benign naevi, 44.7% of atypical naevi and 64.2% of malignant melanomas, whereas the computer identified as asymmetric 6.3%, 33.3% and 82.2%, respectively. Numerical parameters referring to malignant melanomas were significantly higher, both with respect to benign naevi and atypical naevi. The numerical parameters produced could be effectively employed for computer-aided melanoma diagnosis.

A system for the automatic creation of Pictorially Enriched Ontologies is presented, that is ontologies for context-based video digital libraries, enriched by pictorial concepts for video annotation, summarization and similarity-based retrieval. Extraction of pictorial concepts with video clips clustering, ontology storing with MPEG-7, and the use of the ontology for stored video annotation are described. Re-sults on sport videos and TRECVID2005 video material are reported.

In this work, we describe in detail the MPEG-7 Shape Spectrum Descriptor and provide a set of tests with different 3D objects databases. To verify if the literature reported low performance of this descriptor were due to the comparison employed, we also used the Earth Movers Distance which allows much more detailed histograms comparisons. Finally we compare our outcomes with the best results in related work.

In this paper, we propose a system for automatic video summarization at sub-shot level. Our work covers two main aspects: the first is the sub-shot detection, which is performed without a priori constraints on the number or length of the shots. The algorithm is based on color histograms and motion features, and employs fuzzy c-means with variable number of clusters. The second aspect is an in depth discussion on the annotation of summaries with the MPEG-7 standard. Results on mixed genres TV material, from TRECVID videos, are reported.

In this paper an automated assessment of pigment distribution and color areas for melanoma diagnosis is described.

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.

In this paper, we present a system for the assisted creation of Pictorially Enriched Ontologies, that is ontologies for context-based digital libraries enriched by pictorial concepts for video annotation, summarization and similarity based retrieval. Here we detail the approach for video clips clustering and pictorial concepts extraction together with the approach for storing the ontology within the MPEG-7 framework. The clustering is performed by Complete Link hierarchical clustering on color histograms and motion features. Results on Formula 1 TV material are reported.

In this paper, we approach the problem of detecting editing features suitable for video annotation, by paying attention to artifacts and effects introduced in video editing. In particular, a linear transition detection algorithm is presented, which can characterize the transition center and length with high precision. The technique works with sub-frame granularity and is able to include both abrupt cuts and longer dissolves in a single approach. Theoretical justification for the algorithm is provided with an optimization technique for real cases. We present results obtained exploiting the editing features on a Formula 1 video digital library, detecting replays and providing pre classification hints for automatic shot annotation.

Background/purpose: Since in early melanoma (MM) and especially in in situ MM differential structures, which are diagnostic for MM may be lacking, pigment distribution asymmetry represents an important diagnostic feature. Our aim was to automatically assess pigment distribution in images referring to MMs, atypical nevi (AN) and clearly benign nevi (BN), and to evaluate the diagnostic capability of numerical parameters describing a non homogeneous distribution of pigmentation. Methods: An image analysis program enabling the numerical assessment of pigment distribution in melanocytic lesions (ML), based on evaluation and comparison of red, green, blue (RGB) colour components inside image colour blocks, was employed on 459 videomicroscopic digital images, referring to 95 MMs, 76 AN and 288 BN. Results: Significant differences in pigment distribution parameters (mean RGB distance, variance and maximum distance) between the three ML populations were observed, permitting a good discrimination of MMs. On the test set comprising 230 lesion images, the area under the curve value of the receiver operating characteristic curve was 0.933. For a D score equal to 0, corresponding to the best diagnostic accuracy (86.6%), a sensitivity of 87.5% and a specificity of 85.7% were obtained. Conclusion: This original evaluation method for digital pigment distribution, based on mathematical description and comparison of colours in different image blocks, provides numerical parameters to be implemented in image analysis programs for computer-aided MM diagnosis.