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fastER / fast segmentation with Extremal Regions
Permits cell segmentation based on extremal regions. fastER extracts texture and shape features from candidate regions and estimates the likelihood of each to be a cell with a support vector machine (SVM). It uses a divide and conquer approach to calculate an optimal set of non-overlapping candidate regions. The tool allows fast and accurate automated segmentation in large volumes of data and enables on the fly analysis of running experiments.
DISCO / Data Informed Segmentation of Cell Objects
Segments and tracks budding yeast cells. DISCO is a comprehensive framework structured into several stages for integrated identification, segmentation and tracking of cells: (i) identification of physical features of the microfluidic device, (ii) supervised classification to identify cell centers, (iii) segmentation using a morphologically constrained cell-shape model, (iv) incorporation of temporal information to refine cell center prediction and (v) iterative greedy optimization of cell contours.
Allows automatic segmentation through an interactive interface. AutoCellSeg enables researchers to select object features for supervised image segmentation method. This method uses multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. Moreover, this program integrates an algorithm that automatically segments the colonies from the unwanted background in a way that colony forming unit (CFU) boundaries are obtained.
Permits analysis of video microscopy data for long-term experiments. CellStar has been designed to achieve good performance and was compared with six software solutions dedicated to yeast cell segmentation and tracking in bright-field microscopy. It segments yeast cells using bright-field images and tracks their trajectories through time with excellent accuracy. The tool is easy to use and allows the manual correction of segmentation and tracking errors through easy point-and-click procedures.
Root analyzer
Serves for batch processing of large series of images. Root analyzer is designed for extracting and analyzing anatomical traits from root-cross section images. It uses basic knowledge about root morphology, such as cell and tissue size and locations. It lends the program to applications on a range of species. It segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and metaxylem, and produces statistics about the morphological properties of the root cells and tissues.
An automated segmentation method that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.
SMASH / Semi-automatic Muscle Analysis using Segmentation of Histology
Provides an automatic and standardized image segmentation platform. SMASH permits users to measure multiple facets of muscle histology. It carries a high monetary cost and is not specifically designed for skeletal muscle analysis. This tool generates results validated against legacy methods showing largely consistent results between methods for fiber type and centrally nucleated fiber (CNF) percentages. It reduces the border region between adjacent fibers, it is also capable of delineating interstitial space between adjacent fibers when there is an appreciable separation.
IHC Toolbox / Immunohistochemistry Toolbox
Detects the basic components in an Immunohistochemistry (IHC) image. IHC Toolbox was developed using a semi-automatic scheme that is suitable for different kinds of IHC image analysis. The software includes functions for semi-automatic color selection, automatic statistical color detection, automatic nuclei segmentation and automatic gland detection. It allows users to save and transfer their created models and parameters to different users for the reproduction of detection results in different laboratories.
Automates image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 tissue sections. ImmunoRatio uses monoclonal antibodis 6F11, PgR636 and MIB-1 to detect respectively ER, PR and Ki-67. It is able to make blank field correction thank to the utilization of the Calculator Plus plugin. The tool segments the DAB- and hematoxylin-stained nuclei areas from a microscope image, calculates the labeling index, and generates a pseudo-colored result image matching of the segmentation.
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