Article: Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms Journal: International Journal of Signal and Imaging Systems Engineering (IJSISE) 2020 Vol.12 No.1/2 pp.62 - 70 Abstract: Early diagnosis of a brain tumour may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. In this study, first noise removed by median filter and dimensionality of datasets reduced by using random projection transformation (RPT). Next, the pre-processed images are clustered by using K-means and fuzzy c-means (FCM). In the very next step, the clustered images multi-features are fused by different data fusion approaches, and then segment the exact tumour area by using the active contour models such as level set method (LSM) and Chan-Vese (C-V). The performance of clustered based segmentation and fusion-based segmentation in terms of various fusion metrics. The results of both clustered based and fusion-based methods revealed that the CNN fusion-based segmentation performs better than clustered- based segmentation to detect the tumour with low segmentation error and minimal loss of information. Inderscience Publishers - linking academia, business and industry through research

Title: Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms

Authors: K. Rajesh Babu; P.V. Nagajaneyulu; K. Satya Prasad

Addresses: Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Guntur – 522502, Andhra Pradesh, India ' Department of Electronics and Communication Engineering, Sri Mittapalli College of Engineering, Guntur – 522233, Andhra Pradesh, India ' Department of Electronics and Communication Engineering, Vignan's Foundation for Science, Technology and Research, Guntur – 522213, Andhra Pradesh, India

Abstract: Early diagnosis of a brain tumour may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. In this study, first noise removed by median filter and dimensionality of datasets reduced by using random projection transformation (RPT). Next, the pre-processed images are clustered by using K-means and fuzzy c-means (FCM). In the very next step, the clustered images multi-features are fused by different data fusion approaches, and then segment the exact tumour area by using the active contour models such as level set method (LSM) and Chan-Vese (C-V). The performance of clustered based segmentation and fusion-based segmentation in terms of various fusion metrics. The results of both clustered based and fusion-based methods revealed that the CNN fusion-based segmentation performs better than clustered- based segmentation to detect the tumour with low segmentation error and minimal loss of information.

Keywords: dimensionality reduction; level set; Chan-Vese; segmentation distance error; fuzzy c-means; K-means.

DOI: 10.1504/IJSISE.2020.113571

International Journal of Signal and Imaging Systems Engineering, 2020 Vol.12 No.1/2, pp.62 - 70

Accepted: 27 Jan 2021
Published online: 11 Mar 2021 *

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Title: Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms

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