/Brain-Tumor-Detection-

Implemented Artificial Bee Colony Algorithm coupled with fuzzy C means Algorithm using OpenCV and Python. • Combined watershed algorithm for post processing. Extracted tumor from MRI images achieving 80% accuracy

Primary LanguagePython

Project Title : Brain Tumor Detection Using Honey Bee
Submitted by: DEEPTA B S 1PI12IS031
			  HARSHITHA SHREEKA 1PI12IS033
              HARSHITHA S S 1PI12IS034
Folders :
Complete executable code of the project.
Project report document
Final project presentation PPT

Source code execution:

Ui has a button on click of which an image can be uploaded, here the user uploads an image, which is either in jpg, bmp, png or tiff. Python library is used to convert image to 2 dimensional matrix which can be fed to our algorithms.

Preprocessing Button

The image may have noise, disturbances. Therefore we preprocess the image by
removing noise, converting it to grayscale, smoothing the image. We do this using Bilateral filter.
Output: Enhanced image

Processing Button

FCM : Here the enhanced image which has been Pre-processed, is fed into Fuzzy C means algorithm first.
ABC:  Then the optimisation technique of Artificial Bee algorithm is applied to the output of FCM.
Output: Image with detection of high density clusters, predicting them to be tumor.

Post-Processing Button

The image after undergoing processing, is fed to feature extraction algorithm. Hence,
here Watershed algorithm is applied, which colours the image with different colour in different density, thus providing the possible location of brain tumor.
Output: Image with Brain tumor detected.