This brain tumor dataset containing 3064 T1-weighted contrast-inhanced images
from 233 patients with three kinds of brain tumor: meningioma (708 slices), 
glioma (1426 slices), and pituitary tumor (930 slices). Due to the file size
limit of repository, we split the whole dataset into 4 subsets, and achive 
them in 4 .zip files with each .zip file containing 766 slices.The 5-fold
cross-validation indices are also provided.

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This data is organized in matlab data format (.mat file). Each file stores a struct
containing the following fields for an image:

cjdata.label: 1 for meningioma, 2 for glioma, 3 for pituitary tumor
cjdata.PID: patient ID
cjdata.image: image data
cjdata.tumorBorder: a vector storing the coordinates of discrete points on tumor border.
		For example, [x1, y1, x2, y2,...] in which x1, y1 are planar coordinates on tumor border.
		It was generated by manually delineating the tumor border. So we can use it to generate
		binary image of tumor mask.
cjdata.tumorMask: a binary image with 1s indicating tumor region

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This data was used in the following paper:
1. Cheng, Jun, et al. "Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation
and Partition." PloS one 10.10 (2015).
2. Cheng, Jun, et al. "Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector 
Representation." PloS one 11.6 (2016). Matlab source codes are available on github 
https://github.com/chengjun583/brainTumorRetrieval

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#These are the images with (256,256) pixels while all remaining in the 3064 images are (512,512)
#Corrupted images
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1073
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1202
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#Imputed the above images and converted to (512,512)