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Neutrophils derived from induced-differentiated mouse promyleocyte (MPRO) cell lines provide an alternative source of mouse neutrophils for in vitro experiments, substituting for primary mouse neutrophils that are normally obtained by sacrificing mice. One issue with using induced-differentiated MPRO cells (or NEUTs) is that they are usually composed of not only mature neutrophils, but also neutrophil...
Image segmentation is one of the most fundamental steps of image analysis. Almost all vision based systems need segmented images using a good segmentation algorithm. The problem of automatically finding the best algorithm, from a pool of algorithms, on a per image basis has been largely ignored in the vision community. In this paper we present a novel solution to this problem based on classification...
With the advances in satellite sensor technologies as to spatial resolution, the concept of object-based image analysis (OBIA) has been become widely-used in different remote sensing applications and involved the development of some COTS systems. None of the systems available today is able to achieve the accuracy of human image interpreters, however, all of them are able to at least provide a pre-selection...
A region merging segmentation technique is suggested in this paper for the object-based classification of high-spatial resolution imagery. It employs a region growing scheme based on the region adjacency graph (RAG). The proposed algorithm uses directional neighbor-line average feature vectors to improve the quality of segmentation. The feature vector consists of 9 components which includes an observation...
Based on the analysis of the existing problems in pixel-based image processing and object based image analysis (OBIA), this paper proposed a Geographic Unit Sequences (GUS) based image analysis approach, designed to apply geographic ontology knowledge to object based image analysis, so that the image analysis results become the carriers of geographic principle rather than the distribution of land...
Because different types of ground objects have different spatial and spectrum characteristics, automatic and accurate extraction of different thematic objects from remote sensing without spatial conflict and topology conflict is still a challenge. The design of special thematic intelligent Agents to recognize special objects in high-resolution remote sensing images is useful for object extraction...
The land use change detection methods using remote sensing data have been studied for a long term. All of these methods can be divided into three levels: pixel-level, feature-level and knowledge-level. In this paper, the advantages and disadvantages of these methods are analyzed, and a novel approach is proposed to detect the land use changes using the object-based feature consistency analysis. The...
The goal of image segmentation is to cluster pixels into salient image regions, it is the most significant step in image analysis. Thresholding is a simple but effective tool to separate objects from the background, which is one of the most popular algorithms. The artificial bee colony algorithm (ABC) is a recently presented meta-heuristic algorithm, which has been successfully applied to solve many...
Image analysis is still considered as the bottleneck in 2D-gel based expression proteomics analysis for biomarkers discovery. We are presenting a new end-to-end image analysis pipeline of operations that can be fully automated. The pipeline includes image denoising and enhancement based on contourlets, image segmentation into Regions of Interest (ROIs) based on active contours, followed by the analysis...
This paper presents a novel method of visual saliency detection. The use of saliency promises benefits to multimedia applications. However, up to now just few reasonable applications of saliency exist. It is clear that limited accuracy is one of the possible reasons for this. Another reason could be that in general saliency allows us to detect salient regions of the image rather than objects. To fill...
Creating detailed and easy to produce land cover maps of urban residential areas continue to be a challenge. In this research, we discuss and illustrate a methodology for VHR multispectral image segmentation and classification of impervious surfaces. On two levels we have firstly detected impervious and pervious land surfaces and secondly rooftops, pathways and gardens. By using a second image, we...
Per-pixel based methods for spectral classification of remote-sensed images in urban areas are problematic because of the rather high spectral variability in urban materials and the fact that a specific spectral signature might appear in a number of different contexts in the urban landscape. It is by and large the specific contextual arrangement that defines the urban features, not the individual...
In the image analysis, image segmentation is the operation that divides image into set of different segments. The paper deals about common color image segmentation techniques and methods. The advantages and disadvantage of each one will be described in this paper. At the end of the paper, the evaluation criterion will be introduced and applied on the algorithms results. Five most used image segmentation...
Image Processing has a vital role in quality analysis of objects and presumably is one of the best ways of non destructive techniques. This paper aims at detecting flaws if any in the granite blocks by analysis of their digital images. Two common defects, Crack and Mica contamination is studied. The unique characteristics of the defects are studied. To detect the mica contamination k-means segmentation...
Texture segmentation is a complex task in image analysis. Although many works have been done in this area, texture segmentation is still an open research area. The purpose of this paper is to investigate the potential of time signatures generated by a Pulse Coupled Neural Network, PCNN, to perform texture segmentation. Time series features are generated by the PCNN, filtered and then they are clustered...
Image segmentation is one of the most important research areas in image processing and computer vision, and is a key step in image processing and image analysis. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. Based on the measure of medium truth degree, this paper presents a novel image segmentation method by introducing the distance...
Image segmentation is a fundamental step for many image analysis and preprocessing tasks. In segmentation, minimum cross entropy (MCE) based multilevel thresholding is regarded as an effective improvement over the bi-level method. However, it is very time consuming for real-time applications. In this paper, a fast threshold selection method based on bacterial foraging optimization (BFO) algorithm...
In this paper, we present a comprehensive framework to support classification of nuclei in digital microscopy images of diffuse gliomas. This system integrates multiple modules designed for convenient human annotations, standard-based data management, efficient data query and analysis. In our study, 2770 nuclei of six types are annotated by neuropathologists from 29 whole-slide images of glioma biopsies...
We are developing an approach called in toto imaging whose goal is to track all the cell movements and divisions that give rise to an embryo. We can also capture protein expression and localization throughout development using GFP transgenics. Our long term goal is to integrate these data into a “Digital Fish” that shows how the genetic circuits encoded in the genome turn an egg into an embryo. We...
While high-content screening is already playing an important role in drug discovery, a growing number of academic laboratories are applying these techniques to conduct a system-level analysis of biological processes. In this context more complex assays and model systems are being imaged at higher throughput. Examples include co-culture assays, tissues, and entire model systems, as for example zebrafish...
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