A method, system, and apparatus are provided for automated light microscopic for detection of proteins associated with cell proliferative disorders

Automated detection of objects in a biological sample

A method for automated image analysis (Fig. 2, 42) of a biological specimen by histological reconstruction, provided is an automated cell image method for analyzing a biological specimen that has consecutively been stained by either an in situ hybridization (ISH) method, or an immunohistochemistry (IHC) method or a nucleic acid stain, and counterstained. The method couples composite images in an automated manner for processing and analysis to identify structure in tissue that cannot be captured in a single field of view image (Fig. 2, 44) or a single staining technique, the disclosure provides a method for histological reconstruction to analyze many fields of view on potentially many slides (Fig. 2, 37) simultaneously.

Histological reconstruction and automated image analysis

This paper presents implementation of elastic matching technique to recognize online handwritten Gurmukhi characters. We have discussed a process that recognizes characters in two stages. First stage recognizes the strokes, in second stage, character is evaluated on the basis of recognized strokes. Feature are computed to strengthen recognition results. Also, we have discussed a simple way to store data for handwritten strokes and characters. The database for strokes stores script number, stroke number and stroke sample number for every point of a stroke. For 60 writer's and a set of 41 Gurmukhi characters, we have obtained recognition rate as 90.08%.

Online Handwritten Gurmukhi Character Recognition Using Elastic Matching

As part of an architectural modeling project, this paper investigates the problem of understanding and manipulating images of buildings. Our primary motivation is to automatically detect and seamlessly remove unwanted foreground elements from urban scenes. Without explicit handling, these objects will appear pasted as artifacts on the model. Recovering the building facade in a video sequence is relatively simple because parallax induces foreground/background depth layers, but here we consider static images only. We develop a series of methods that enable foreground removal from images of buildings or brick walls. The key insight is to use a prioriknowledge about grid patterns on building facades that can be modeled as Near Regular Textures (NRT). We describe a Markov Random Field (MRF) model for such textures and introduce a Markov Chain Monte Carlo (MCMC) optimization procedure for discovering them. This simple spatial rule is then used as a starting point for inference of missing windows, facade segmentation, outlier identification, and foreground removal.

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Analysis of Building Textures for Reconstructing Partially Occluded Facades

A fundamental step of microarray image analysis is the detection of the grid structure for the accurate location of each spot, representing the state of a given gene in a particular experimental condition. This step is known as gridding and belongs to the class of deformable grid matching problems which are well known in literature. Most of the available microarray gridding approaches require human intervention; for example, to specify landmarks, some points in the spot grid, or even to precisely locate individual spots. Automating this part of the process can allow high throughput analysis. This paper focuses on the development of a fully automated procedure for the problem of automatic microarray gridding. It is grounded on the Bayesian paradigm and on image analysis techniques. The procedure has two main steps. The first step, based on the Radon transform, is aimed at generating a grid hypothesis; the second step accounts for local grid deformations. The accuracy and properties of the procedure are quantitatively assessed over a set of synthetic and real images; the results are compared with well-known methods available from the literature

A Deformable Grid-Matching Approach for Microarray Images

A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which represents the spatial coordinates of the grid nodes. Knowledge of how grid nodes are depicted in the observed image is described through the observation model. The prior consists of a node prior and an arc (edge) prior, both modeled as Gaussian MRFs. The node prior models variations in the positions of grid nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing grid nodes and grid-node artifacts and the method accommodates a wide range of grid distortions including: large-scale warping, varying row/column spacing, as well as nonrigid random fluctuations of the grid nodes. The methodology is demonstrated in two case studies concerning (1) localization of DNA signals in hybridization filters and (2) localization of knit units in textile samples.

K. Hartelius

Papers

Bayesian grid matching