Segmentation and Analysis Emphasizing Neonatal MRI Brain Images Using Machine Learning Techniques

Saritha Saladi, Yepuganti Karuna, Srinivas Koppu, Gudheti Ramachandra Reddy, Senthilkumar Mohan, Saurav Mallik () and Hong Qin ()
Additional contact information
Saritha Saladi: School of Electronics Engineering, VIT-AP University, Vijayawada 522237, India
Yepuganti Karuna: SENSE, Vellore Institute of Technology, Vellore 632014, India
Srinivas Koppu: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Gudheti Ramachandra Reddy: SENSE, Vellore Institute of Technology, Vellore 632014, India
Senthilkumar Mohan: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Saurav Mallik: Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA
Hong Qin: Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA

Mathematics, 2023, vol. 11, issue 2, 1-20

Abstract: MRI scanning has shown significant growth in the detection of brain tumors in the recent decade among various methods such as MRA, X-ray, CT, PET, SPECT, etc. Brain tumor identification requires high exactness because a minor error can be life-threatening. Brain tumor disclosure remains a challenging job in medical image processing. This paper targets to explicate a method that is more precise and accurate in brain tumor detection and focuses on tumors in neonatal brains. The infant brain varies from the adult brain in some aspects, and proper preprocessing technique proves to be fruitful to avoid miscues in results. This paper is divided into two parts: In the first half, preprocessing was accomplished using HE, CLAHE, and BPDFHE enhancement techniques. An analysis is the sequel to the above methods to check for the best method based on performance metrics, i.e., MSE, PSNR, RMSE, and AMBE. The second half deals with the segmentation process. We propose a novel ARKFCM to use for segmentation. Finally, the trends in the performance metrics (dice similarity and Jaccard similarity) as well as the segmentation results are discussed in comparison with the conventional FCM method.

Keywords: MRI segmentation; histogram equalization; CLAHE; BPDFHE; neonatal brain; ARKFCM; FCM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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