Abstract
Inaccurate diagnosis has caused early death of people suffering from brain tumor and this has been proved by research. Since the human brain is a complex structure, tumor like illness is very difficult to detect. In this project the concept of cuckoo search optimization is used to detect the tumor in brain. Here, histogram of the input image is calculated and the peak values are randomly taken as input in the cuckoo search algorithm then histogram thresholding is done on the optimized image. Thresholding is done to detect the tumor region based on the optimal threshold value obtained from cuckoo search algorithm. After the binary image is obtained morphological operations as post-processing is done to distinct the tumor region. Generally the tumor of the brain is recognized by radiologists through a far reaching examination of images of MR which takes significantly a more extended time. The main aim is to build up a demonstrative framework using cuckoo search and histogram thresholding, that would help the radiologist to have a quick assessment with respect to the nearness or nonappearance of tumor. Here the Cuckoo Search based technique is not only implemented but is also compared with other existing brain tumor detection techniques.
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Acknowledgements
The calculation is tested on 30 standard images using MATLAB R2015b and different types of tumor images are downloaded from the website of Harvard Medical School where they have provided MR images for different slices of brain from top view [13], from the website of radiopaedia [14] and from the website of brainweb which provides custom MR Simulator to generate ground truth image [15]. The images provided are free to use.
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Bhakat, S., Periannan, S. (2019). Brain Tumor Detection Using Cuckoo Search Algorithm and Histogram Thresholding for MR Images. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 851. Springer, Singapore. https://doi.org/10.1007/978-981-13-2414-7_9
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