<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Brain tumors and cancers are life-threatening diseases to human beings and have been on the rise. If undetected, they are deadly. With the advent of advanced medical technology, it has become imperative to accurately spot and identify these tumors at the earliest.</p><p>The manuscript aims at providing an accurate method to detect and segment brain tumors from MRI scans. This is achieved by implementing watershed segmentation and threshold algorithm paired with pre and post image processing techniques. Apart from detecting the tumor region, the proposed process also enhances image quality by noise removal techniques and image quality improvement. These results give promising values when verified using several evaluation parameters such as Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM) and Peak Signal-to-Noise Ratio (PSNR) and stand out among the other similar pre-existing algorithms that they are compared with in a comparative analysis.</p></abstract>

Brain tumors and cancers are life-threatening diseases to human beings and have been on the rise. If undetected, they are deadly. With the advent of advanced medical technology, it has become imperative to accurately spot and identify these tumors at the earliest.The manuscript aims at providing an accurate method to detect and segment brain tumors from MRI scans. This is achieved by implementing watershed segmentation and threshold algorithm paired with pre and post image processing techniques. Apart from detecting the tumor region, the proposed process also enhances image quality by noise removal techniques and image quality improvement. These results give promising values when verified using several evaluation parameters such as Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM) and Peak Signal-to-Noise Ratio (PSNR) and stand out among the other similar pre-existing algorithms that they are compared with in a comparative analysis.

Performance evaluation of brain tumor detection using watershed Segmentation and thresholding

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Method to detect and segment brain tumors from MRI scans: Mishra et al.

Brain tumors and cancers are life-threatening diseases to human beings and have been on the rise. If undetected, they are deadly. With the advent of advanced...

Different approaches
Parameters	Proposed algorithm	(Tarhini and Reda, 2020)	(Oo and Aung Soe, 2014)
PSNR	18.4537	9.73795	18.97545
SSIM	0.79545	0.1833	0.29315
FSIM	0.92583	0.4439	0.75115
Entropy	5.8122	0.91185	4.8462
NAE	0.368	0.9941	0.28255
NCC	0.8801	0.37585	0.9122

Parameter
Image	PSNR	SSIM	FISM	Entropy	NAE	NCC
Image 1	14.548	0.709	0.961	6.275	0.449	0.875
Image 2	14.694	0.659	0.939	6.033	0.501	0.804
Image 3	18.021	0.612	0.88	5.301	0.436	0.855
Image 4	14.26	0.725	0.8666	6.159	0.463	0.775
Image 5	15.775	0.708	0.881	4.8	0.618	0.712
Image 6	19.624	0.809	0.876	5.337	0.288	0.906
Image 7	18.961	0.748	0.941	6.376	0.3	0.948
Image 8	29.673	0.934	0.96	4.473	0.08	0.991
Image 9	19.89	0.886	0.939	6.51	0.271	0.931
Image 10	17.931	0.806	0.937	6.493	0.344	0.846

Performance evaluation of brain tumor detection using watershed Segmentation and thresholding

Published Online: Nov 24, 2021

Page range: 1 - 12

Received: Aug 20, 2021

DOI: https://doi.org/10.21307/ijssis-2021-020

Keywords
Brain tumor, Evaluation parameters, MRI, Threshold algorithm, Watershed segmentation

© 2021 Shruti Mishra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Comparative analysis.

Statistical analysis.

Performance evaluation of brain tumor detection using watershed Segmentation and thresholding

Published Online: Nov 24, 2021

Page range: 1 - 12

Received: Aug 20, 2021

DOI: https://doi.org/10.21307/ijssis-2021-020

KeywordsBrain tumor, Evaluation parameters, MRI, Threshold algorithm, Watershed segmentation

© 2021 Shruti Mishra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Comparative analysis.

Statistical analysis.

Keywords
Brain tumor, Evaluation parameters, MRI, Threshold algorithm, Watershed segmentation