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Medical image computing typically operates on uniformly sampled data with regular x-y-z spatial spacing (images in 2D and volumes in 3D, generically referred to as images). At each sample point, data is commonly represented in integral form such as signed and unsigned short (16-bit), although forms from unsigned char (8-bit) to 32-bit float are ...
These images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the US. This is a 21 class land use image dataset meant for research purposes. There are 100 images for each class. 2,100 Image chips of 256x256, 30 cm (1 foot) GSD Land cover classification 2010 [171]
IDL – often used to view medical images; ImageJ; InVesalius – free, open source software that can be used to view DICOM images and transform DICOM image stacks to 3D models and export them to .STL; IrfanView; MicroDicom – free DICOM viewer for Windows. Noesis – free DICOM importer and exporter with 3D visualization for Windows.
Medical optical imaging is the use of light as an investigational imaging technique for medical applications, pioneered by American Physical Chemist Britton Chance. Examples include optical microscopy , spectroscopy , endoscopy , scanning laser ophthalmoscopy , laser Doppler imaging , optical coherence tomography , and transdermal optical imaging .
Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues . Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease.
ImageJ supports image stacks, a series of images that share a single window, and it is multithreaded, so time-consuming operations can be performed in parallel on multi-CPU hardware. ImageJ can calculate area and pixel value statistics of user-defined selections and intensity-thresholded objects.
Microscope image processing is a broad term that covers the use of digital image processing techniques to process, analyze and present images obtained from a microscope. Such processing is now commonplace in a number of diverse fields such as medicine , biological research , cancer research , drug testing , metallurgy , etc.
If the goal is a medical diagnostic, then histology applications will often fall into the realm of digital pathology or automated tissue image analysis, which are sister fields of bioimage informatics. The same computational techniques are often applicable, but the goals are medically- rather than research-oriented.