1 - 22 of 22 results

DFCNet / Deep Fully Convolutional neural Network

Identifies and classifies the pulmonary nodules in CT-Scan images. DFCNet is a generic classifier built on deep fully convolutional neural network. The classification is separated into two classes of nodule (diseased-malignant of benign) and non-nodule (normal). Images classified as nodules are then categorized into four lung cancer stages, but this computer assisted detection (CAD) system can be used for the detection of other types of cancer because this software is a generic method for detection.

HMSLIC / Hexagonal clustering and Morphological optimize Sequential Linear Iterative Clustering algorithm

Provides a superpixel segmentation algorithm focused on the lung CT images. HMSLIC uses a hexagonal method to obtain more regular segmented superpixels and improve edge information preservation. Besides, it uses morphology to combine bright superpixels to eliminate the influence of bright blob-like blood vessels with the aim of reducing the complexity of the subsequent calculation of distance.


Provides a free volume (3D image) viewer and segmentation tool. BrainSeg3D is a graphic application that make segmentation of volumes more accurate by providing tools for semi-automated segmentation combined with a user friendly graphic interface. This application is based on Seg3D, a free volume segmentation and processing tool. It was developed for medical professionals who need to perform image analysis as part of their research or for researchers working in the field of image analysis.

Simpleware ScanIP

Provides a software environment for comprehensively processing 3D image data (MRI, CT, micro-CT, FIB-SEM…). Simpleware ScanIP offers powerful image visualisation, analysis, segmentation, and quantification tools. It includes video recording features and options to export surface models/meshes from segmented data for CAD and 3D printing. Additional modules are available for exporting CAE meshes, integrating image data and CAD, exporting NURBS and calculating effective material properties from scans.

ITK / Insight Segmentation and Registration Toolkit

Allows to perform registration and segmentation for image analysis. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. ITK uses a model of software development known as Extreme Programming. The sampled representation is an image acquired from such medical instrumentation as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or ultrasound scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

Visible patient

Provides a list of tools to aid researchers in reading, interpreting, reporting, and treatment planning. Visible patient includes detection and labeling tools of organ segments. It contains basic imaging tools for: (1) general images; (2) including 2D viewing, (3) volume rendering and 3D volume viewing, (4) orthogonal Multi-Planar Reconstructions (MPR), (5) image fusion, (6) surface rendering, (7) measurements, (8) reporting, (9) storing, (10) general image management; and (11) administration.