Marco Manfredi

Dynamic Optical Coherence Tomography in Dermatology

Optical coherence tomography (OCT) represents a non-invasive imaging technology, which may be applied to the diagnosis of non-melanoma skin cancer and which has recently been shown to improve the diagnostic accuracy of basal cell carcinoma. Technical developments of OCT continue to expand the applicability of OCT for different neoplastic and inflammatory skin diseases. Of these, dynamic OCT (D-OCT) based on speckle variance OCT is of special interest as it allows the in vivo evaluation of blood vessels and their distribution within specific lesions, providing additional functional information and consequently greater density of data. In an effort to assess the potential of D-OCT for future scientific and clinical studies, we have therefore reviewed the literature and preliminary unpublished data on the visualization of the microvasculature using D-OCT. Information on D-OCT in skin cancers including melanoma, as well as in a variety of other skin diseases, is presented in an atlas. Possible diagnostic features are suggested, although these require additional validation.

In vivo, micro-morphological vascular changes induced by topical brimonidine studied by Dynamic optical coherence tomography.

Brimonidine is a selective α2 adrenergic receptor agonist with potent vasoconstrictive activity topically used for treatment of facial flushing and erythema caused by rosacea. Direct evidence for the in vivo morphology changes in skin vessels induced by topical application of brimonidine is limited. Dynamic optical coherence tomography is a novel technology that combines conventional OCT with information on flow and thereby provides supplementary information about the microvasculature. Dynamic OCT is non‐invasive and creates high‐resolution in vivo images of skin to a depth of maximum 2 mm.

A fast approach for integrating ORB descriptors in the bag of words model

In this paper we propose to integrate the recently introduces ORB descriptors in the currently favored approach for image classification, that is the Bag of Words model. In particular the problem to be solved is to provide a clustering method able to deal with the binary string nature of the ORB descriptors. We suggest to use a k-means like approach, called k-majority, substituting Euclidean distance with Hamming distance and majority selected vector as the new cluster center. Results combining this new approach with other features are provided over the ImageCLEF 2011 dataset.

GOLD: Gaussians of Local Descriptors for image representation☆

Abstract The Bag of Words paradigm has been the baseline from which several successful image classification solutions were developed in the last decade. These represent images by quantizing local descriptors and summarizing their distribution. The quantization step introduces a dependency on the dataset, that even if in some contexts significantly boosts the performance, severely limits its generalization capabilities. Differently, in this paper, we propose to model the local features distribution with a multivariate Gaussian, without any quantization. The full rank covariance matrix, which lies on a Riemannian manifold, is projected on the tangent Euclidean space and concatenated to the mean vector. The resulting representation, a Gaussian of Local Descriptors (GOLD), allows to use the dot product to closely approximate a distance between distributions without the need for expensive kernel computations. We describe an image by an improved spatial pyramid, which avoids boundary effects with soft assignment: local descriptors contribute to neighboring Gaussians, forming a weighted spatial pyramid of GOLD descriptors. In addition, we extend the model leveraging dataset characteristics in a mixture of Gaussian formulation further improving the classification accuracy. To deal with large scale datasets and high dimensional feature spaces the Stochastic Gradient Descent solver is adopted. Experimental results on several publicly available datasets show that the proposed method obtains state-of-the-art performance.

Detection of static groups and crowds gathered in open spaces by texture classification

Texture and motion information used for static crowd detection.Gradient Co-occurrence matrix descriptors for texture analysis.One class SVM to learn crowd patches. A surveillance system specifically developed to manage crowded scenes is described in this paper. In particular we focused on static crowds, composed by groups of people gathered and stayed in the same place for a while. The detection and spatial localization of static crowd situations is performed by means of a One Class Support Vector Machine, working on texture features extracted at patch level. Spatial regions containing crowds are identified and filtered using motion information to prevent noise and false alarms due to moving flows of people. By means of one class classification and inner texture descriptors, we are able to obtain, from a single training set, a sufficiently general crowd model that can be used for all the scenarios that shares a similar viewpoint. Tests on public datasets and real setups validate the proposed system.

A complete system for garment segmentation and color classification

In this paper, we propose a general approach for automatic segmentation, color-based retrieval and classification of garments in fashion store databases, exploiting shape and color information. The garment segmentation is automatically initialized by learning geometric constraints and shape cues, then it is performed by modeling both skin and accessory colors with Gaussian Mixture Models. For color similarity retrieval and classification, to adapt the color description to the users’ perception and the company marketing directives, a color histogram with an optimized binning strategy, learned on the given color classes, is introduced and combined with HOG features for garment classification. Experiments validating the proposed strategy, and a free-to-use dataset publicly available for scientific purposes, are finally detailed.

Optical coherence tomography of basal cell carcinoma: density and signal attenuation.

Basal cell carcinoma (BCC) is the most prevalent malignancy in Caucasians. Optical coherence tomography (OCT) is a non‐invasive optical imaging technology using the principle of interferometry. OCT has shown a great potential in diagnosing, monitoring, and follow‐up of BCC. So far most OCT studies on the subject of BCC have had a qualitative focus, i.e. on morphological analysis of the OCT images. The aim of this study was to explore the use of quantitative OCT measurements, density, and attenuation coefficient in BCC lesions as a way to improve the OCT evaluation of BCC.

Modeling local descriptors with multivariate gaussians for object and scene recognition

Common techniques represent images by quantizing local descriptors and summarizing their distribution in a histogram. In this paper we propose to employ a parametric description and compare its capabilities to histogram based approaches. We use the multivariate Gaussian distribution, applied over the SIFT descriptors, extracted with dense sampling on a spatial pyramid. Every distribution is converted to a high-dimensional descriptor, by concatenating the mean vector and the projection of the covariance matrix on the Euclidean space tangent to the Riemannian manifold. Experiments on Caltech-101 and ImageCLEF2011 are performed using the Stochastic Gradient Descent solver, which allows to deal with large scale datasets and high dimensional feature spaces.

Covariance of Covariance Features for Image Classification

In this paper we propose a novel image descriptor built by computing the covariance of pixel level features on densely sampled patches and encoding them using their covariance. Appropriate projections to the Euclidean space and feature normalizations are employed in order to provide a strong descriptor usable with linear classifiers. In order to remove border effects, we further enhance the Spatial Pyramid representation with bilinear interpolation. Experimental results conducted on two common datasets for object and texture classification show that the performance of our method is comparable with state of the art techniques, but removing any dataset specific dependency in the feature encoding step.

Image Classification with Multivariate Gaussian Descriptors

Techniques based on Bag Of Words approach represent images by quantizing local descriptors and summarizing their distribution in a histogram. Differently, in this paper we describe an image as multivariate Gaussian distribution, estimated over the extracted local descriptors. The estimated distribution is mapped to a high-dimensional descriptor, by concatenating the mean vector and the projection of the covariance matrix on the Euclidean space tangent to the Riemannian manifold. To deal with large scale datasets and high dimensional feature spaces the Stochastic Gradient Descent solver is adopted. The experimental results on Caltech-101 and ImageCLEF2011 show that the method obtains competitive performance with state-of-the art approaches.