Image classification software tools | Magnetic resonance imaging analysis
The conventional method in medicine for brain MR images classification and tumor detection is human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious in accuracies classification. The MR images data is by nature, a huge, complex and cognitive process. Accurate diagnosis of MR images data is not an easy task and is always time consuming. In some extreme scenario, diagnosis with wrong result and delay in delivery of a correct diagnosis decision could occur due to the complexity and cognitive process of which it is involve.
Enables largely automated processing of magnetic resonance images (MRI) of the human brain. BrainSuite provides a sequence of low-level operations that can produce accurate brain segmentations in clinical time. It produces classified brain volumes that can be useful for quantitative studies of different regions of the brain. The tool consists of several modules that performs skull and scalp removal, nonuniformity correction, tissue classification, and object topology correction.
Enables easy pattern classification of neuroimaging data and offering a broad assortment of machine learning algorithms and feature selection methods. MANIA provides interfaces to common third party software libraries, especially Matlab pattern classification tools. It is easy to use for non-experts, users are encouraged to study the basics of the different methods especially when using advanced and complex algorithms.
Consists of a tissue classification method for magnetic resonance imaging (MRI) scans of 6-month old infants. NeuroMTL iSEG is a program that includes two techniques: (1) an extended training dataset that works by applying a standard tissue classification technique to scans of 24-month old infants from separate longitudinal dataset (ACE-IBIS); and (2) a deep learning method to train a 3D U-Net network to perform tissue segmentation on scans of 6-month old infants, first on ACE-IBIS scans, and then on iSEG scans.