Medical Imaging Toolbox™ provides apps, functions, and workflows for designing and testing diagnostic imaging applications. You can perform 3D rendering and visualization, multimodal registration, and segmentation and labeling of radiology images. The toolbox also lets you train predefined deep learning networks (with Deep Learning Toolbox™).
You can import, preprocess, and analyze radiology images from various imaging modalities, including projected X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine (PET, SPECT). The Medical Image Labeler app lets you semi-automate 2D and 3D labeling for use in AI workflows. You can perform multimodal registration of medical images, including 2D images, 3D surfaces, and 3D volumes. The toolbox provides an integrated environment for end-to-end computer-aided diagnosis and medical image analysis.
Importing Medical Imaging Data
Read image data and metadata from specialized medical file formats, such as DICOM, NIfTI, and NRRD, that store data describing the patient, imaging procedure, and spatial referencing.
Visualizing 2D Images and 3D Volumes
Use interactive tools to visualize 2D and 3D medical imaging data. Generate and render 3D surfaces and volumes.
Ground Truth Labeling
Use the Medical Image Labeler app to interactively label ground truth data, semi-automate or automate the labeling process, and export labeled data for AI workflows.
Preprocessing and Augmentation
Improve image quality using preprocessing techniques and improve the effectiveness of deep learning networks using random intensity augmentation to expand the training dataset.
Medical Image Registration
Compare multimodal medical images, volumes, or surfaces using image registration to align them to a common coordinate system.
Segment 2D images or 3D volumes into regions such as bones, tumors, or organs using traditional or deep learning techniques, and evaluate the accuracy of the regions.
“Diagnosis of Thyroid Nodules from Medical Ultrasound Images with Deep Learning ”By Eunjung Lee, School of Mathematics and Computing (CSE), Yonsei University
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