index.html

---
layout: default
title: A high throughput image processing application
subtitle: An image processing application
description: ImageC is a high throughput image processing application. Especially designed for usage in biological science.
body_class: landing nowrap
---

<section class="welcome">
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			<h1>a high throughput<br/>image processing application</h1>
			<p>Quantify thousands of images<br/>using your individual processing pipelines</p>
			<a class="button transparent arrow-down dark" href="{{ site.baseurl }}/docs/installation/">Installation</a>
			<a class="button blue" href="{{ site.baseurl }}/docs/">Documentation</a>
		</div>
		<div class="slideshow window">
			<img src="images/spinner/cell-detection.webp" class="active"/>
			<img src="images/spinner/landing-page.webp" />
			<img src="images/spinner/control_0_0.webp" />
		  </div>
		</div>
</section>

<section class="whyimagec">
	<div class="wrap">
		<h1>Highlights</h1>
		<div class="flexwrap">
			<div class="featurebox">
				<div class="icon speed"></div>
				<h3>Fast processing pipelines for big data sets</h3>
				<p>ImageC is implemented in C++, one of the fastest programming languages and efficiently uses all available CPU resources (multi threading support). With the focus on hight throughput analyzes, ImageC allows processing times down to 0.2 seconds per image, allowing the analysis of thousands of images in a reasonable time.</p>
				<a href="{% link docs/stable/fundamentals/pipelines.md %}" class="textbutton arrow-right">Read more</a>
			</div>
			<div class="featurebox">
				<div class="icon microscope"></div>
				<h3>Analysis of fluorescence and brightfield images</h3>
				<p>ImageC enables object detection and quantification in fluorescence images. Analysis and cross channel quantification can be applied to any number of channels. e.g. Measurement of fluorescence intensity per cell.</p>
				<a href="{% link docs/stable/fundamentals/image_formats.md %}" class="textbutton arrow-right">Read more</a>
			</div>
			<div class="featurebox">
				<div class="icon liver"></div>
				<h3>Analysis of histological images</h3>
				<p>ImageC enables the analysis of histological images due the support of whole slide images, taken by fluorescence and light microscopy. There is no need to split or preprocess the images. ImageC takes care of all the necessary preprocessing steps, including tile and pyramid support.</p>
				<a href="{% link docs/stable/fundamentals/image_formats.md %}#big-images" class="textbutton arrow-right">Read more</a>
			</div>
			<div class="featurebox">
				<div class="icon bioformats"></div>
				<h3>BioFormats support</h3>
				<p>All common image formats used by different microscope manufacturers are supported thanks to BioFormats integration.</p>
				<a href="{% link docs/stable/fundamentals/image_formats.md %}" class="textbutton arrow-right">Read more</a>
			</div>
			<div class="featurebox">
				<div class="icon layer"></div>
				<h3>Multi channel images and Videos</h3>
				<p>ImageC automatically extracts underlying image infos (e.g. channel infos, Z-stack infos, etc. ) using OME-XML which allows to directly use multi channel images handling z-stack as well as the possibility to analyze complete videos (t-tack).</p>
				<a href="{% link docs/stable/fundamentals/image_formats.md %}#image-planes" class="textbutton arrow-right">Read more</a>
			</div>
			<div class="featurebox">
				<div class="icon database"></div>
				<h3>Big data organization</h3>
				<p>ImageC stores all the pipeline results into an integrated in-process SQL database (DuckDb) using a predefined data structure. The flexibility of the database matched with an easy to use GUI enables basic data postprocessing and comparison.</p>
				<a href="{% link docs/stable/first_steps/results.md %}" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon download"></div>
				<h3>Data export</h3>
				<p>The data are stored as an compressed database file and can be exported for further processing to R or Excel. Custom export templates can be used for the creation of individual data sets.</p>
				<a href="{% link docs/stable/first_steps/results.md %}#data-export" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon plus"></div>
				<h3>Image/Data grouping</h3>
				<p>Automated image grouping based on well formats, image names using regex or directory structure can help to organize data output. AVG, MEDIAN, MAX, MIN, STDEV, SUM and CNT are automatically calculated from multiple images within a group.</p>
				<a href="{% link docs/stable/first_steps/operation.md %}#image-grouping-tab" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon filter"></div>
				<h3>Image/Data Filtering</h3>
				<p>ImageC allows to define data filters, removing images from the report based on customized criteria. e.g. remove Images without cells.</p>
				<a href="{% link docs/stable/commands/filtering/threshold_filter.md %}" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon table"></div>
				<h3>Heatmaps</h3>
				<p>ImageC can generate heatmaps of images or image groups (e.g. plates. wells), enabling a quick assessment of the data.</p>
				<a href="{% link docs/stable/first_steps/results.md %}#image-detail-view"class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon live"></div>
				<h3>Interactive results</h3>
				<p>Browse through your results to see the selected objects in the original image. The interactive mode allows really to observe each individual detected object within the origin image.</p>
				<a href="{% link docs/stable/first_steps/results.md %}#interactive-mode" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon slider"></div>
				<h3>Pipeline creation</h3>
				<p>ImageC enables the creation of individual image processing pipelines for object detection. A set of widely used image processing algorithms ported from ImageJ, including background subtraction algorithms, filtering, edge detection and manual as well as auto-threshold are implemented and can be used for pipeline creation.</p>
				<a href="{% link docs/stable/fundamentals/pipelines.md %}" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon show"></div>
				<h3>Live preview</h3>
				<p>ImageC offers a live preview enabling to monitor the impact of all image processing steps within the pipelines and thereby provides transparent and understandable object detection.</p>
				<a href="{% link docs/stable/first_steps/operation.md %}#pipelines-tab" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon chip"></div>
				<h3>AI driven object detection</h3>
				<p>In addition to classical image processing and thresholding algorithms ImageC supports object detection and classification based on AI. ImageC can read ONNX container as well as Pytorch pt format using U-Net, Cyto3, StarDist or YOLO5 model architecture</p>
				<a href="{% link docs/stable/commands/classification/classifier_ai.md %}" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon mlp"></div>
				<h3>Build in AI model training</h3>
				<p>ImageC allows to train simple AI models for pixel classification directly from within the application. The trained models can be used at any place in the pipeline.</p>
				<a href="/about_imagec#free" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon image"></div>
				<h3>Generation of control images</h3>
				<p>ImageC generates user defined control images, for documentation and internal control.</p>
				<a href="{% link docs/stable/commands/object_processing/save_control_image.md %}" class="textbutton arrow-right">Read more</a>
			</div>

			<div class="featurebox">
				<div class="icon code"></div>
				<h3>Pipeline templates</h3>
				<p>EVAnalyzer and its successor ImageC where created within a research group focused on extracellular vesicles. ImageC provides powerful pipelines especially for single vesicle imaging applications.</p>
				<a href="#" class="textbutton arrow-right">Read more</a>
			</div>
			<div class="featurebox">
				<div class="icon distance"></div>
				<h3>Distance measurement</h3>
				<p>The ImageC distance measurement feature automatically calculates distances across different object classes. Together with time frame support, this enables the quantification of low-level object tracking.</p>
				<a href="{% link docs/stable/commands/measurement/measure_distance.md %}" class="textbutton arrow-right">Read more</a>
			</div>
		</div>
	</div>
</section>

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