Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm | Zendy

Prince Ebenezer | Zendy; Henry NunooMensah | Zendy; Richard Junior | Zendy; Joyce Raissa | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm

Author(s) -

Prince Ebenezer,

Henry NunooMensah,

Richard Junior,

Joyce Raissa

Publication year - 2018

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/ijca2018917915

Subject(s) - thresholding , computer science , segmentation , artificial intelligence , pattern recognition (psychology) , algorithm , image (mathematics)

This paper deals with the implementation of a simple algorithm for automatic brain tumor segmentation. Brain tumor is commonly diagnosed by Computer tomography and Magnetic Resonance Imaging in clinical treatment. The paper uses Simple Linear Iterative Clustering (SLIC) to segment brain images according to their spatial and color proximities. The ratio of the mean and variance of the image pixels are determined in order to obtain an optimum threshold value. Region merging after thresholding was carried out. The final output image was an image with tumor sections circled out. The segmentation adheres to boundaries and the procedure is fast and reproducible.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research