Biomedical subjects

Giovanni F Crosta

Publications and source records attributed to Giovanni F Crosta.

4 recordsLinked to original sources

PubMed2007

Scoring CFU-GM colonies in vitro by data fusion: a first account.

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 distortion- and 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.

Algorithms↗

PubMed2005

Microscopy and quantitative morphology of aluminum silicate nanoparticles grown on organic templates.

Biomimetic synthesis of ceramic materials is increasing in popularity because it offers many advantages. In this work, aluminum silicate nanoparticles were obtained on a self-assembled organic multilayer at near room temperature (30 < or = T < or = 50 degrees C) and at atmospheric pressure. Morphological and microanalytical characterization was carried out by means of transmission electron microscopy and subsequent image analysis. The roles of some process parameters such as template type, reactant concentration, [Al]:[Si] molar ratio, number of initiation steps (IS) of mineralization, and reaction time (rt) were assessed by comparing images, diffraction patterns, and EDX spectra. Generally, the Si-rich phase exhibited higher crystallinity, whereas the Al-rich phase was mostly amorphous. Crystal structure resulted with rt > or = 4 days for template-grown materials. Images of materials obtained at T = 50 degrees C, rt = 3 days, and 1 < or = IS < or = 4 were further analyzed by "spectrum enhancement," an algorithm based on the Fourier transform. Morphological indicators were extracted from suitably processed power spectral densities, a correlation matrix was formed, and multivariate statistics was carried out. Visual differences in nanoaggregate morphology were quantitatively translated. Materials were ranked by the spatial uniformity of nanoparticle distribution: the most uniform aggregates were those grown on templates by IS > or = 2. Univariate statistics validated the conclusion: the particles of those same materials had a narrower size distribution and sharper edges. This last property has been ascribed to crystalline structure, independently demonstrated by diffraction patterns.

Aluminum↗

PubMed2004

Multivariate analysis and classification of two-dimensional angular optical scattering patterns from aggregates.

Two-dimensional light-scattering patterns from aggregates have undergone feature extraction followed by multivariate statistical analysis. The aggregates are comprised of primary particles of varying shape and size. Morphological descriptors (features) were extracted by a nonlinear filtering algorithm (spectrum enhancement) and then processed by principal component analysis and discriminant function analysis. The analysis was performed on two data sets, one in which the aggregates had a fixed primary particle size but varied in overall dimension and another in which the aggregate size was fixed but the primary particle size varied. Classification of the samples was performed adequately, providing some distinction among the limited classes that were analyzed.

Journal Article↗

PubMedDate not supplied

Classifying structural alterations of the cytoskeleton by spectrum enhancement and descriptor fusion.

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.

Algorithms↗