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 (cvind.mat).

1. Data structure
   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, in [x1, y1, x2, y2,...], (xi, yi) are planar coordinates on the tumor border. They were 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 the tumor region

1. How to convert .mat format to other image formats?
   For example, you can use the MATLAB code below to convert the .mat images to .jpg images (or other formats by simply changing jpg to the format you want). The resulting jpg images will be stored in three folders, each for one class.

input_folder = 'your input path';
output_folder = 'your output path';
file_list = dir(fullfile(input_folder, '*.mat'));
for i = 1:numel(file_list)
file_name = file_list(i).name;
file_path = fullfile(input_folder, file_name);

mat_data = load(file_path);  
cjdata = mat_data.cjdata;

im1 = double(cjdata.image); 
min1 = min(im1(:));
max1 = max(im1(:));
im = uint8(255/(max1-min1)*(im1-min1));
label = cjdata.label;

label_folder = fullfile(output_folder, num2str(label));
if ~exist(label_folder, 'dir')
    mkdir(label_folder); 
end

[~, file_name_base, ~] = fileparts(file_name);
output_file_path = fullfile(label_folder, strcat(file_name_base, '.jpg'));

imwrite(im, output_file_path);

end

1. The acquisition protocol used to acquire the images
   All images in our dataset (T1-weighted contrast-enhanced MRI) were acquired after Gd-DTPA injection at Nanfang Hospital, Guangzhou, China and General Hospital, Tianjin Medical University, China from 2005.9 to 2010.10. The images have an in-plane resolution of 512 × 512 with pixel dimensions of 0.49 × 0.49 mm^2 . The slice thickness is 6 mm and the slice gap is 1 mm. The Gd dose was 0.1 mmol/kg at a rate of 2 ml/s.

No further details are available due to the long passage of time.

1. Citations
   This data was used in the following papers:
2. Cheng, Jun, et al. "Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition." PloS one 10.10 (2015).

3. 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

4. Contact information
   Jun Cheng
   School of Biomedical Engineering
   Shenzhen University, Shenzhen, China
   Email: chengjun583@qq.com