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An autochthonous case of lymphocutaneous sporotrichosis caused by Sporothrix schenckii is reported. The patient developed skin lesions localized along the lymphatics that appeared after he suffered an injury while collecting wicker canes in marshy water. The fungus was identified as Sporothrix schenckii by MALDI-TOF and sequencing. Phylogenetic analysis was also performed. Low MIC values were detected for all tested echinocandins and azoles except for fluconazole. The patient was treated with itraconazole without significant improvement. A regression of lesions was observed after 3 months of therapy with voriconazole. Few cases of sporotrichosis have been reported in Europe. However, several cases of sporotrichosis have been described in Italy. The incidence of sporotrichosis in Italy may be underestimated and microbiologists, and clinicians must be aware of this fungal infection.

A classifier capable of ranking structural alterations of the cytoskeleton is developed. Images of cytoskeletal microtubules obtained from the epifluorescence microscopy of primary culture rat hepatocytes are analyzed. Morphological descriptors are extracted by contour and mass fractal analysis, direct methods, and spectrum enhancement. All methods are designed and tuned to make the extracted morphological descriptors insensitive to absolute fluorescence intensities. Spectrum enhancement is a nonlinear filter that involves spatial differentiation of the gray-scale image followed by conversion of power spectral density to the logarithmic scale and averaging over arcs in the reciprocal domain. Enhanced spectra exhibit local maxima that correspond to the structured microtubule bundles of a normal cytoskeleton. Descriptor fusion for classification is achieved by means of multivariate analysis. The classifier is trained by image sets representing normal ("negative control") microtubules and those altered by exposure to a fungicide at the highest dose of the experiment design. Some sensitivity and validation tests, including discriminant functions analysis, are applied to the classifier. The latter is applied to recognize images of microtubules not used in the training stage and comes from treatments at lower concentrations and shorter times. As a result, structural alterations are ranked and structural recovery after treatment is quantified. The method has potential use in quantitative, morphology-based tests on the cytoskeleton treated either by anticancer drugs or by cytotoxic agents.

Quantitative morphology has already been shown by these authors to describe the processes of cytoskeletal disorganisation and re-organisation related to the exposure of cultured cells to xenobiotics [G F Crosta, C Urani, L Fumarola, Estimating Structural Damage of the Cytoskeleton by means of Morphological Descriptors, Proc. SPIE 5461 Biophotonics New Frontier: From Genome to Proteome, M D Faupel, P P Meyrueis, Eds., SPIE: Bellingham, WA, 2004, to appear]. Namely, an automatic classifier was developed, which relies on morphological descriptors. Epifluorescence images of cytoskeletal microtubules were processed by Fourier analysis (“spectrum enhancement”), fractal analysis (mass and contour) and spatial differentiation aimed at extracting morphological descriptors. The classifier was successfully applied to rank the structural damage and re-organisation of microtubules. This work focusses on the automatic training of the classifier by a two-layer hierarchical scheme. On the first layer the parameters which control the extraction of the Fourier descriptors are chosen according to some optimality condition (either by solving a saddle point problem or maximising specificity and selectivity); reduction of the Fourier descriptors is obtained by selecting the first few principal components. The latter are passed on to the second layer and merged with descriptors from fractal analysis and spatial differentiation. As a consequence, classification improves. Internal and external (by linear discriminant analysis) validation results will also be presented. The scheme is being applied to images of microtubules and microfilaments from different toxicological experiments.

The morphology of cytoskeletal microtubules has been analyzed by fractal, direct and spectral methods. Sets of images were obtained from the epifluorescence microscopy of primary cultures of rat hepatocytes treated with fungicide concentrations of 5O and 25 µg/ml for 2h. The morphological descriptors extracted by said methods included contour and mass fractal dimension, total variation, the L1-norm of the Laplacian and properties of the "enhanced spectrum". The latter is obtained by suitably processing the logarithm of power spectral density with the aim of separating image structure (low spatial frequency) from texture (high spatial frequency). Descriptors were fused by principal components analysis. A classification algorithm was trained to tell undisturbed (control) cytoskeletal structures from those treated at the higher dose. The eigenvector matrix of the trained classifier was used to rank structures treated at the lower dose: from regression on the set centroid coordinates a tentative relation between the first principal component (the "response") and dose has been obtained. The same ranking procedure was applied to structures recovering from injury (24h after exposure to the higher dose) and the extent of recovery has been quantified. The paper includes a possible interpretation of some morphological descriptors and their role in automatic classification.

Direct methods (spatial differentiation), fractal analysis and spectral analysis ("spectrum enhancement") have been applied to extract morphological descriptors from images of cytoskeletal microtubules. Images had been obtained from experiments on cultured cells (rat hepatocytes). Principal components analysis has been applied to morphological descriptors. An image classifier has thus been trained to tell normal (control) microtubule structures from those treated by a given concentration of a fungicide for a given time. Validation has been performed on sets of new images of the same two classes. Then the classifier has been used to rank the morphology of microtubules treated at lower doses and to quantify structural recovery after exposure. The paper is the first account of extensive morphological classification of microtubules and paves the way to a dose-response relationship based on quantitative morphology.

Morphological alteratiions of the tubulin cytoskeleton indicate functional damage. Primary rat hepatocyte cultures, both untreated (control) and treated (i.e., exposed to known concentrations of Benomyl) have been imaged with indirect immuno-fluorescence to visualise microtubule bundles. Four methods of image analysis have been applied: contour fractal analysis, mass fractal analysis, spatial differentiation, and non-linear filtering in the reciprocal domain. Morphological descriptors have been obtained, which have then submitted to multivariate statistical analysis for discrimination between control and treated classes. Discrimination has been statistically significant.

The cytoskeletal microtubules (MTs) of rat hepatocytes treated by BenomylTM (a fungicide) were imaged by means of immunofluorescent staining and optical microscopy. Images of untreated, or control (C), and of treated (T) cells were processed both by fractal and FOURIER analysis. The C-MTs had contour fractal dimensions higher (≥ 1.4) than those of TMTs (≤ 1.3). FOURIER analysis included computation of the anisotropy of power spectral density, angle averaging and "spectrum enhancement", which corresponds to the application of a (pseudo)differential operator to the image. Enhanced spectra were interpolated by a polynomial, q, of degree 39, from which morphological descriptors were extracted. Descriptors from FOURIER analysis made image classification possible. Principal components analysis was applied to the descriptors. In the plane of the first two components, {z1, z2}, the minimum spanning tree was drawn. Images of T- MTs formed a single cluster, whereas images of C-MTs formed two clusters, C1 and C2. The component z1 correlated positively with the local maxima and minima of q, which reflected differences between T and C in power spectral density in the 1 to 2000 cycles/mm spatial frequency band. The difference between C1 and C2 was ascribed to anisotropy of the MT bundles. The implemented image classifier is capable of telling differences in cytoskeletal organization. As a consequence the method can become a tool for testing cytotoxicity and for extracting quantitative information about intracellular alterations of various origin.

Diagnostic images at all biological scales benefit from quantitative morphological analysis which can lead to their classification and recognition. With reference to an image from an optical microscope, quantitative morphology consists of extracting from the raw image, by a suitable algorithm, some numerical indicators or "descriptors". The latter replace the image and represent it for subsequent analysis by algorithms capable of: classification, recognition, feature extraction and other higher level functions. Herewith the extraction of morphological descriptors relies on the "spectrum enhancement" algorithm: the discrete Fourier transform of the image undergoes some nonlinear operatons and comparison with a model power spectral density. As a result the graph of a function of wavenumber, the enhanced spectrum, is obtained, which carries information about the structure and texture of the image. Enhanced spectra are submitted to a classifier based on multivariate statistics, which can be trained, validated and applied to recognize new images. The whole procedure will be illustrated by applications at different scales: a) (sub-cell) cytoskeletal structures altered by xenobiotics; b) (colony) cell colonies ("foci") from in vitro neoplastic transformation assays; c) (tissue) cross sections of reconstituted human corneal epithelia. The results of diagnostic and pathological interest, which will be presented, include the quantitative estimation of morphological alterations, dose-response relationships and recovery from damage. The same paradigm can apply to images acquired by different (optical) instruments and is intended for integration with other methods, the eventual goal being the high content analysis of biological data.

To investigate the transmission of Helicobacter pylori in the perinatal period.

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Objective. In vitro models of hematopoiesis used in investigative hematopathology and in safety studies on candidate drugs, involve clonogenic assays on colony-forming unit granulocyte macrophage (CFU-GM). These assays require live and unstained colonies to be counted. Most laboratories still rely on visual scoring, which is time-consuming and error-prone. As a consequence, automated scoring is highly desired. An algorithm that recognizes and scores CFU-GM colonies by data fusion has been developed. Some preliminary results are presented in this article. Methods. CFU-GM assays were carried out on hematopoietic progenitors (human umbilical cord blood cells) grown in methylcellulose. Colony images were acquired by a digital camera and stored. Results. The classifier was designed to process images of layers sampled from a three-dimensional (3D) domain and forming a stack. Structure and texture information was extracted from each image. Classifier training was based on a 3D colony model applied to the image stack. The number of scored colonies (assigned class) was required to match the count supplied by the human expert (class of belonging). The trained classifier was validated on one more stack and then applied to a stack with overlapping colonies. Scoring in distortionand caustic-affected border areas was also successfully demonstrated. Because of hardware limitations, compact colonies in some cases were missed. Conclusions. The industry¿s scoring methods all rely on structure alone and process 2D data. Instead, the classifier here fuses data from a whole stack and is capable, in principle, of high-throughput screening.

These authors have been developing for some years a variety of morphological classifiers, which analyse images, extract descriptors by FOURIER analysis, fractal analysis and spatial differentiation, fuse these descriptors by means of multivariate statistics. Classifiers have been trained, validated and applied to recognizing patterns belonging to new classes. One of the most relevant application has been the quantitative morphology of microtubule organisation. Results, which have been described in a number of publications, have consisted of: a) the quantitative assessment of structural damage caused by xenobiotics and the ensueing recovery, and b) the estimation of dose- and time- response relations. This paper, in addition to presenting a survey of the classification methods and the related results, will focus on some instructive class-wide and cell-wise statistical properties deduced from the first principal component only. These properties lead to three questions about the dose-response behaviour of microtubules which are still open.

In order to evaluate the potential hematotoxicity of xenobiotics, including candidate anti-cancer drugs, in vitro models of hematopoiesis are used, which involve clonogenic assays on CFU-GM (Colony Forming Unit-Granulocyte-Macrophage). These assays require live and unstained colonies to be counted. Most laboratories still rely on visual scoring, which is time consuming and error prone. As a consequence automated scoring is highly desired. A classification algorithm aimed at emulating the colony recognition and scoring capabilities of a human expert has been developed. A first account will be given herewith. Assays were carried out on CFU-GM progenitors derived from human umbilical cord blood cells and grown in methylcellulose. A three-dimensional (3-D) medium is essential for these assays to simulate the clonogenetic process which takes place in bone marrow. Stacks of images representing slices of a 3-D domain were acquired. Structure and texture information was extracted from each image. Classifier training was based on a 3-D colony model applied to the image stack. The number of scored colonies (assigned class) was required to match the count supplied by the human expert (class of belonging). Successful applications to scoring colonies, which partially overlap and/or are masked by caustics, are described. Whereas the industry's scoring methods all rely on image structure alone and process 2-D data, the classifier described herewith takes texture into account and fuses 3-D data from a whole stack

Recently, these authors developed a heterogeneous, one-level image classifier (CH) based on morphological descriptors from direct domain analysis (spatial differentiation), fractal analysis and "spectrum enhancement" (a kind of non-linear filtering). Classifier C H was applied to epi-fluorescence microscope images of cytoskeletal microtubules and was trained to recognize structural alterations of the cytoskeleton in various circumstances. The application dealt with images of rat hepatocytes (rh). The scope of this paper is twofold: a) to investigate different classifier architectures, which include the multiobjective optimization of some image analysis parameters by means of suitable algorithms; b) to apply said classifiers to new sets of images obtained from mouse fibroblasts (mf) and HepG2 (hg) cells. Image sets from control and treated cell cultures are analyzed. Classifier CH is applied to mf microtubules. A new classifier entirely relying on "spectrum enhancement" (although on different descriptors) is developed and applied to rh and hg images. From the latter classifier, by bringing in descriptors from direct domain and fractal analysis, a hierarchical classifier is derived and applied to rh images. Results are compared. Classifier performance is expressed in terms of sensitivity, specificity and information contents of the first three principal components

The cytoskeleton is a network of proteins which structurally and dynamically organise the cytoplasm of living cells. Microtubules are among its constituents. Morphological alterations of microtubules are related to functional impairment. Therefore cytoskeletal morphology is a valuable indicator of cell injury and functionality. This paper focuses on the comparison between normal and altered cytoskeletal microtubules by means of image analysis and classification with the aim of replacing visual assessment. Morphology has been quantified by the extraction of some descriptors yielded by spatial differentiation, fractal analysis and Fourier analysis followed by non-linear filtering. The principal component analysis of these descriptors has led to image recognition and has been applied to hepatocytes and fibroblasts exposed to some xenobiotics. In the case of hepatocytes, images have been ranked according to the severity of cytoskeletal damage, a dose-response relation has been derived from the regression of the first principal component and the percentage of structural recovery after exposure has been estimated.

The loss of myelin sheath which surrounds a nerve fiber is usually quantified by the visual scoring of unaltered axons and the measuring of cross-sectional areas. An attempt at the automated classification of morphological alterations affecting de-myelinated nerve fibers is presented herewith. Thin cross-sections of rat sciatic nerves, both unaltered (controls) and affected by constriction (Bennet - Xie ligature) have been stained and imaged by optical microscopy. Image processing has included: spatial differentiation, fractal analysis, and Fourier transformation followed by non-linear filtering. A set of quantitative morphological descriptors has been derived, then submitted to multivariate statistical analysis, the goal being discrimination between control and injured nerve bundles. The first discrimination results are described.