TY - GEN
T1 - Multi-objective Particle Swarm Optimization Based Enhanced Fuzzy C-Means Algorithm for the Segmentation of MRI Data
AU - Singh, Munendra
AU - Asha, C. S.
AU - Sharma, Neeraj
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Fuzzy c-means algorithm and its variants are popular for the segmentation of magnetic resonance imaging (MRI) data. The enhanced fuzzy c-means approach is one among them that comprises weighted local spatial data. However, the quantity of spatial data added with input MRI image differs and that depends on the noise content and sequence of MRI. Hence, the value of weight factor needs to be chosen appropriately and automatically to attain the accurate segmentation results. In this perspective, the current work focuses to generate optimum weight values and presents an optimized enhanced fuzzy c-means algorithm for MRI data. The proposed method utilizes the multi-objective particle swarm optimization to control the weight parameter that leads to maximum segmentation accuracy. The new approach is tested and validated on a standard simulated BrainWeb MRI dataset. The outcome shows that the proposed approach is flexible and robust to noise content as compared to the conventional algorithms.
AB - Fuzzy c-means algorithm and its variants are popular for the segmentation of magnetic resonance imaging (MRI) data. The enhanced fuzzy c-means approach is one among them that comprises weighted local spatial data. However, the quantity of spatial data added with input MRI image differs and that depends on the noise content and sequence of MRI. Hence, the value of weight factor needs to be chosen appropriately and automatically to attain the accurate segmentation results. In this perspective, the current work focuses to generate optimum weight values and presents an optimized enhanced fuzzy c-means algorithm for MRI data. The proposed method utilizes the multi-objective particle swarm optimization to control the weight parameter that leads to maximum segmentation accuracy. The new approach is tested and validated on a standard simulated BrainWeb MRI dataset. The outcome shows that the proposed approach is flexible and robust to noise content as compared to the conventional algorithms.
UR - https://www.scopus.com/pages/publications/85121747182
UR - https://www.scopus.com/pages/publications/85121747182#tab=citedBy
U2 - 10.1007/978-981-16-2761-3_90
DO - 10.1007/978-981-16-2761-3_90
M3 - Conference contribution
AN - SCOPUS:85121747182
SN - 9789811627606
T3 - Lecture Notes in Electrical Engineering
SP - 1031
EP - 1041
BT - Recent Trends in Electronics and Communication - Select Proceedings of VCAS 2020
A2 - Dhawan, Amit
A2 - Tripathi, Vijay Shanker
A2 - Arya, Karm Veer
A2 - Naik, Kshirasagar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on VLSI, Communication and Signal processing, VCAS 2020
Y2 - 9 October 2020 through 11 October 2020
ER -