Here are two ready-to-use online brain atlases I use (see links). They should contain data from lesion studies as well. Check out the Neurosynth database first.

http://neurosynth.org/

http://www.thehumanbrain.info/brain/

I recommend you to use the brain tumor database from the MIC- CAI 2013 Challenge on Multimodal Brain Tumor Segmentation. This is  a very good database of brain tumor (high grade an low grade) with segementation results and groud truth.

I suggest to try with brain web

http://brainweb.bic.mni.mcgill.ca/brainweb/selection_normal.html

I found database here......

http://www.bic.mni.mcgill.ca/~laurence/data/data.html

https://medpix.nlm.nih.gov is the site you can get help

i cant find the database can you mention them clearly

Normal brain images are not available can you help!?

You can check this database :

https://humandbs.biosciencedbc.jp/data-use

You can check this database :

http://openfmri.org/dataset/

https://www.mr-tip.com/serv1.php?type=db1&dbs=Brain%20MRI

https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection

https://ieee-dataport.org/competitions/brats-miccai-brain-tumor-dataset

Hi;

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans.

BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas.

Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’19 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.

Hi;

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans.

BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas.

Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’19 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.

The domain of brain tumor analysis has effectively utilized the concepts of medical image processing, particularly on MR images, to automate the core steps, i.e. extraction, segmentation, classification for proximate detection of tumor. Research is more inclined towards MR for its non-invasive imaging properties. Computer aided diagnosis or detection systems are becoming challenging and are still an open problem due to variability in shapes, areas, and sizes of tumor