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An improved GABOR wavelet transform and rough k-means clustering algorithm for MRI BRAIN tumor image segmentation by B. Chinna Rao; K. Raju; G. Ramesh Babu; Chandra Sekhar Pittala is a Computer Science article available to read on EtoBox.

What is An improved GABOR wavelet transform and rough k-means clustering algorithm for MRI BRAIN tumor image segmentation about?

Our Proposed research is about tumor identification in the human brain. Here, MRI images are considered as the key factor in this research. There are five stages included in this proposed research and the very first stage is pre-processing followed by feature extraction, feature selection, classification, and finally segmentation. The input images are changed into transforming domain, then it happens with the assistance of Improved Gabor Wavelet Transform (IGWT). By Oppositional fruit fly algorithm (OFFA), the features called GLCM reside features are extracted and predominant features are also got chosen. To confirm whether the images look normal or abnormal, the chosen features are handed over to the SVM (Support Vector Machine) classifier. Once the classification process gets completed, the abnormally looking images are then picked out and the images are sent to the next process called segmentation. We used a rough k-means algorithm for the successful segmentation process. In comparison with other existing researches, our work seems structured and efficient. And Based on some evaluation metrics we estimated our efficiency and performance.

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Author

B. Chinna Rao; K. Raju; G. Ramesh Babu; Chandra Sekhar Pittala

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)