You can use the Dice Similarity Coefficient (DSC) and measure the overlap between the results obtain by two methods.

Here is a reference to the original paper:

Dice, L.R., 1945. Measures of the amount of ecologic association between species. Ecology 26 (3), 297–302.

You can also calculate the co-occurence table and for that obtain Cohen's kappa coefficient.

You can use the Dice Similarity Coefficient (DSC) and measure the overlap between the results obtain by two methods.

Here is a reference to the original paper:

Dice, L.R., 1945. Measures of the amount of ecologic association between species. Ecology 26 (3), 297–302.

You can also calculate the co-occurence table and for that obtain Cohen's kappa coefficient.

Along with Dice coefficient you can also use Jaccard Index, Ratio of False positives, Ratio of False negatives, Sensitivity and Specificity. You can find the details on the following

http://sve.bmap.ucla.edu/

Thanks #Aikaterini Kotrotsou . It is a good idea 

Thanks # Archana Chaudhari. for your answer

I think that all the well known indices mentionned by ARCHANA are currently used as the validation indices in the image segmentation. but especially for brain tumor segmentation in MRI images it is recommended to see the experts opinions on radiology to reinforce the obtained results. MRI segmentation is very sensitive to the quality of the input image and to the sgmentation algorithm. at this time I haven't seen the best, ROC method is also the one to assess it may be the best for some images and less for others..

Thank you D.Omar for your answer