• VIA Group Public Databases : - In collaboration with the I-ELCAP group we have established two public image databases that contain lung CT images in the DICOM format together with documentation of abnormalities by radiologists.
  • National Biomedical Imaging Archive (NBIA):-

    Lung Image Database Consortium (LIDC)
    Reference Image Database to Evaluate Response (RIDER)
    Breast MRI
    Lung PET/CT
    Neuro MRI
    CT Colongraphy
    Virtual Colonoscopy
    Osteoarthritis Initiative
    PET/CT phantom scan collection
  • OASIS :The Open Access Series of Imaging Studies (OASIS) is a project aimed at making MRI data sets of the brain freely available to the scientific community. By compiling and freely distributing MRI data sets, we hope to facilitate future discoveries in basic and clinical neuroscience.
  • ADNI:- Alzheimer’s Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer’s disease. ADNI researchers collect, validate and utilize data such as MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors for the disease.

  • FITBIR:- The Federal Interagency Traumatic Brain Injury Research (FITBIR) informatics system: MRI, PET, Contrast, and other data on a range of TBI conditions

  • STARE:-STructured Analysis of the Retina: This research concerns a system to automatically diagnose diseases of the human eye.

  • Cancer Digital Slide Archive :-Whole-slide images from The Cancer Genome Atlas's (TCGA) glioblastoma multiforme (GBM) samples

  • The Cancer Imaging Archive:- The image data in The Cancer Imaging Archive (TCIA) is organized into purpose-built collections of subjects. The subjects typically have a cancer type and/or anatomical site (lung, brain, etc.) in common.

  • Johns Hopkins Medical Institute:-DTI Atlases: adults, children, ...

  • Duke Center for In Vivo Microscopy:- Small animal MRI, CT, ...

  • UCI Machine Learning Repository: The father of internet data archives for all forms of machine learning.

  • Computer Vision Online Image Archive:- Large listing of multiple databases in computer vision and biomedical imaging

  • Cornell Visualization and Image Analysis (VIA) group:- Provides a list of available databases, many of which are also listed here.

  • UT Health Science Center Image Collections:- List of medical images, atlases, and databases available on the web.


  • CMU Face Datasets -  Testing images for the face detection task, and the facial expression database

  • Public Figures Face Database - The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects. Thus, there is large variation in pose, lighting, expression, scene, camera, imaging conditions and parameters, etc.

  • FGnet Face and Gesture Recognition - FGnet is the European working group on face and gesture recognition funded by the E.C. IST program. The objectives of FGnet are to of encourage development of a technology for face and gesture recognition.

  • MIT Face Dataset - This is a database of faces and non-faces, that has been used extensively at the Center for Biological and Computational Learning at MIT. It is freely available for research use.

  • Pedestrian dataset from MIT - The training database of people was generated from color images and video sequences taken in Boston and Cambridge in a variety of seasons using several different digital cameras and video recorders.


  • Biometrics Ideal Test :-Biometrics Ideal Test (or BIT for short) is a website for biometric database sharing and algorithm evaluation. You can download publicly available iris, face, fingerprint, palmprint, multi-spectral palm and handwriting databases and submit your algorithms online for third-part testing and certification free of charge.
  • Computer Vision Test Images

  • Hopkins 155 Dataset :- The Hopkins 155 Dataset has been created with the goal of providing an extensive benchmark for testing feature based motion segmentation algorithms. It contains video sequences along with the features extracted and tracked in all the frames. The ground-truth segmentation is also provided for comparison purposes. The data is stored in the .MAT file format. 
  • Image Segmentation Dataset - A dataset that i prepared for testing image segmentation algorithms. All images are synthetic of size 128×128