A novel triple-level combinational framework for brain anomaly segmentation to augment clinical diagnosis-Reference-Cited by-同舟云学术

A novel triple-level combinational framework for brain anomaly segmentation to augment clinical diagnosis

Published:2021-10-06 Issue: Volume: Page:1-16
ISSN:2168-1163
Container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Author:

Natarajan Senthilkumar¹,Govindaraj Vishnuvarthanan²,Venkata Rao Narayana Ravipudi³,Zhang Yu-Dong⁴,Murugan Pallikonda Rajasekaran¹,Kandasamy Karunanithi⁵,Ejaz Khurram⁶

Affiliation:

1. Department of ECE, Kalasalingam Academy of Research and Education (Kalasalingam University), Srivilliputtur, India

2. Department of BME, Kalasalingam Academy of Research and Education (Kalasalingam University), Srivilliputtur, India

3. Department of ME, Sardar Vallabhbhai National Institute of Technology, Surat, India

4. School of Informatics, University of Leicester, Leicester, UK

5. Department of EEE, Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, Avadi, India

6. Department of CS, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

Publisher

Informa UK Limited

Subject

Computer Science Applications,Radiology Nuclear Medicine and imaging,Biomedical Engineering,Computational Mechanics

Link

https://www.tandfonline.com/doi/pdf/10.1080/21681163.2021.1986858

Reference54 articles.

1. Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

2. Brain tumour classification using two‐tier classifier with adaptive segmentation technique

3. Hybrid methods for feature extraction for breast masses classification

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