Basak Oztan

The regulation of focal adhesion complex formation and salivary gland epithelial cell organization by nanofibrous PLGA scaffolds

Nanofiber scaffolds have been useful for engineering tissues derived from mesenchymal cells, but few studies have investigated their applicability for epithelial cell-derived tissues. In this study, we generated nanofiber (250 nm) or microfiber (1200 nm) scaffolds via electrospinning from the polymer, poly-l-lactic-co-glycolic acid (PLGA). Cell-scaffold contacts were visualized using fluorescent immunocytochemistry and laser scanning confocal microscopy. Focal adhesion (FA) proteins, such as phosphorylated FAK (Tyr397), paxillin (Tyr118), talin and vinculin were localized to FA complexes in adult cells grown on planar surfaces but were reduced and diffusely localized in cells grown on nanofiber surfaces, similar to the pattern observed in adult mouse salivary gland tissues. Significant differences in epithelial cell morphology and cell clustering were also observed and quantified, using image segmentation and computational cell-graph analyses. No statistically significant differences in scaffold stiffness between planar PLGA film controls compared to nanofibers scaffolds were detected using nanoindentation with atomic force microscopy, indicating that scaffold topography rather than mechanical properties accounts for changes in cell attachments and cell structure. Finally, PLGA nanofiber scaffolds could support the spontaneous self-organization and branching of dissociated embryonic salivary gland cells. Nanofiber scaffolds may therefore have applicability in the future for engineering an artificial salivary gland.

Novel Image Analysis Approach Quantifies Morphological Characteristics of 3D Breast Culture Acini with Varying Metastatic Potentials

Prognosis of breast cancer is primarily predicted by the histological grading of the tumor, where pathologists manually evaluate microscopic characteristics of the tissue. This labor intensive process suffers from intra- and inter-observer variations; thus, computer-aided systems that accomplish this assessment automatically are in high demand. We address this by developing an image analysis framework for the automated grading of breast cancer in in vitro three-dimensional breast epithelial acini through the characterization of acinar structure morphology. A set of statistically significant features for the characterization of acini morphology are exploited for the automated grading of six (MCF10 series) cell line cultures mimicking three grades of breast cancer along the metastatic cascade. In addition to capturing both expected and visually differentiable changes, we quantify subtle differences that pose a challenge to assess through microscopic inspection. Our method achieves 89.0% accuracy in grading the acinar structures as nonmalignant, noninvasive carcinoma, and invasive carcinoma grades. We further demonstrate that the proposed methodology can be successfully applied for the grading of in vivo tissue samples albeit with additional constraints. These results indicate that the proposed features can be used to describe the relationship between the acini morphology and cellular function along the metastatic cascade.

Follicular lymphoma grading using cell-graphs and multi-scale feature analysis

We present a method for the computer-aided histopathological grading of follicular lymphoma (FL) images based on a multi-scale feature analysis. We analyze FL images using cell-graphs to characterize the structural organization of the cells in tissues. Cell-graphs represent histopathological images with undirected and unweighted graphs wherein the cytological components constitute the graph nodes and the approximate adjacencies of the components are represented with edges. Using the features extracted from nuclei- and cytoplasm-based cell-graphs, a classifier defines the grading of the follicular lymphoma images. The performance of this system is comparable to that of our recently developed system that characterizes higher-level semantic description of tissues using model-based intermediate representation (MBIR) and color-textural analysis. When tested with three different classifiers, the combination of cell-graph based features with the MBIR and color-textural features followed by a multi-scale feature selection is shown to achieve considerably higher classification accuracies than any set of these feature sets can achieve separately.

Continuous Phase Modulated Halftones and Their Application to Halftone Data Embedding

We propose a generalization of periodic clustered-dot halftones, wherein the phase of the halftones is modulated using a secondary signal. The process is accomplished using an analytic halftone threshold function and allows halftones to be generated with different phase variation in different regions of the printed page. We demonstrate that ensuring continuity of the phase assures that the resulting halftone images are free from visible artifacts despite the modulation in phase. We present, applications of the proposed method to halftone data embedding, wherein the changes in phase or in frequency encode the embedded information. For the frequency embedding, using continuous phase modulation, we also consider the limitations on signals that are embedded within our framework. For both applications, we demonstrate how the embedded signals may be recovered from the printed halftones either using the image as a self-reference which reveals the watermark when it is shifted and overlaid with itself or by employing a separate transparency mask

Cell-Based Multi-Parametric Model of Cleft Progression during Submandibular Salivary Gland Branching Morphogenesis

Cleft formation during submandibular salivary gland branching morphogenesis is the critical step initiating the growth and development of the complex adult organ. Previous experimental studies indicated requirements for several epithelial cellular processes, such as proliferation, migration, cell-cell adhesion, cell-extracellular matrix (matrix) adhesion, and cellular contraction in cleft formation; however, the relative contribution of each of these processes is not fully understood since it is not possible to experimentally manipulate each factor independently. We present here a comprehensive analysis of several cellular parameters regulating cleft progression during branching morphogenesis in the epithelial tissue of an early embryonic salivary gland at a local scale using an on lattice Monte-Carlo simulation model, the Glazier-Graner-Hogeweg model. We utilized measurements from time-lapse images of mouse submandibular gland organ explants to construct a temporally and spatially relevant cell-based 2D model. Our model simulates the effect of cellular proliferation, actomyosin contractility, cell-cell and cell-matrix adhesions on cleft progression, and it was used to test specific hypotheses regarding the function of these parameters in branching morphogenesis. We use innovative features capturing several aspects of cleft morphology and quantitatively analyze clefts formed during functional modification of the cellular parameters. Our simulations predict that a low epithelial mitosis rate and moderate level of actomyosin contractility in the cleft cells promote cleft progression. Raising or lowering levels of contractility and mitosis rate resulted in non-progressive clefts. We also show that lowered cell-cell adhesion in the cleft region and increased cleft cell-matrix adhesions are required for cleft progression. Using a classifier-based analysis, the relative importance of these four contributing cellular factors for effective cleft progression was determined as follows: cleft cell contractility, cleft region cell-cell adhesion strength, epithelial cell mitosis rate, and cell-matrix adhesion strength.

Per-separation clustered-dot color halftone watermarks: separation estimation based on spatial frequency content

A framework for clustered-dot color halftone watermarking is considered, wherein watermark patterns are embedded in individ- ual colorant halftones prior to printing and embedded watermarks are detected from scans of the printed images after obtaining esti- mates of the individual halftone separations. The principal challenge in this methodology arises in the watermark detection phase. Typical three-channel RGB scanner systems do not directly provide good es- timates of the four CMYK colorant halftones that are commonly used in color printing systems. To address this challenge, we propose an estimation method that, when used with suitably selected halftone periodicities, jointly exploits the differences in the spatial periodicities and the color (spectra) of the halftone separations to obtain good estimates of the individual halftones from conventional RGB scans. We demonstrate the efficacy of this methodology experimentally us- ing continuous phase modulation for the embedding of independent visual watermark patterns in the individual halftone separations. Wa- termarks detected from the estimates of halftone separations ob- tained using the proposed estimation method have a much higher contrast than those detected directly. We also evaluate the accuracy of the estimated halftones through simulations and demonstrate that the proposed estimation method offers high accuracy.

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

BackgroundComputational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.MethodsIn order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the nodes and the approximate adjacency of cells are represented with edges. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.ResultsWe built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.ConclusionsTogether, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.

Removal of artifacts from JPEG compressed document images

We present a segmentation-based post-processing method to remove compression artifacts from JPEG compressed document images. JPEG compressed images typically exhibit ringing and blocking artifacts, which can be objectionable to the viewer above certain compression levels. The ringing is more dominant around textual regions while the blocking is more visible in natural images. Despite extensive research, reducing these artifacts in an effective manner still remains challenging. Document images are often segmented for various reasons. As a result, the segmentation information in many instances is available without requiring additional computation. We have developed a low computational cost method to reduce ringing and blocking artifacts for segmented document images. The method assumes the textual parts and pictorial regions in the document have been separated from each other by an automatic segmentation technique. It performs simple image processing techniques to clean out ringing and blocking artifacts from these regions.

Continuous Phase-Modulated Halftones

A generalization of periodic clustered-dot halftones is proposed, wherein the phase of the halftone spots is modulated using a secondary signal. The process is accomplished by using an analytic halftone threshold function that allows halftones to be generated with controlled phase variation in different regions of the printed page. The method can also be used to modulate the screen frequency, albeit with additional constraints. Visible artifacts are minimized/eliminated by ensuring the continuity of the modulation in phase. Limitations and capabilities of the method are analyzed through a quantitative model. The technique can be exploited for two applications that are presented in this paper: (a) embedding watermarks in the halftone image by encoding information in phase or in frequency and (b) modulating the screen frequency according to the frequency content of the continuous tone image in order to improve spatial and tonal rendering. Experimental performance is demonstrated for both applications.

Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model

This study considers the problem of describing and predicting cleft formation during the early stages of branching morphogenesis in mouse submandibular salivary glands (SMG) under the influence of varied concentrations of epidermal growth factors (EGF). Given a time-lapse video of a growing SMG, first we build a descriptive model that captures the underlying biological process and quantifies the ground truth. Tissue-scale (global) and morphological features related to regions of interest (local features) are used to characterize the biological ground truth. Second, we devise a predictive growth model that simulates EGF-modulated branching morphogenesis using a dynamic graph algorithm, which is driven by biological parameters such as EGF concentration, mitosis rate, and cleft progression rate. Given the initial configuration of the SMG, the evolution of the dynamic graph predicts the cleft formation, while maintaining the local structural characteristics of the SMG. We determined that higher EGF concentrations cause the formation of higher number of buds and comparatively shallow cleft depths. Third, we compared the prediction accuracy of our model to the Glazier-Graner-Hogeweg (GGH) model, an on-lattice Monte-Carlo simulation model, under a specific energy function parameter set that allows new rounds of de novo cleft formation. The results demonstrate that the dynamic graph model yields comparable simulations of gland growth to that of the GGH model with a significantly lower computational complexity. Fourth, we enhanced this model to predict the SMG morphology for an EGF concentration without the assistance of a ground truth time-lapse biological video data; this is a substantial benefit of our model over other similar models that are guided and terminated by information regarding the final SMG morphology. Hence, our model is suitable for testing the impact of different biological parameters involved with the process of branching morphogenesis in silico, while reducing the requirement of in vivo experiments.