loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: B. S. Harish 1 ; S. V. Aruna Kumar 1 ; Francesco Masulli 2 and Stefano Rovetta 2

Affiliations: 1 Sri Jayachamarajendra College of Engineering, India ; 2 University of Genova, Italy

Keyword(s): Segmentation, Clustering, Fuzzy C Means, Ant Colony Optimization, RSKFCM.

Related Ontology Subjects/Areas/Topics: Applications ; Clustering ; Evolutionary Computation ; Fuzzy Logic ; Medical Imaging ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: Segmentation is a fundamental preprocessing step in medical imaging for diagnosis and surgical operations planning. The popular Fuzzy C-Means clustering algorithm perform well in the absence of noise, but it is non robust to noise as it makes use of the Euclidean distance and does not exploit the spatial information of the image. These limitations can be addressed by using the Robust Spatial Kernel FCM (RSKFCM) algorithm that takes advantage of the spatial information and uses a Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives a good result, the main drawback of this method is the inability of obtaining good minima for the objective function as it happens for many other clustering algorithms. To improve the efficiency of RSKFCM method, in this paper, we proposed the Ant Colony Optimization algorithm based RSKFCM (ACORSKFCM). By using the Ant Colony Optimization, RSKFCM initializes the cluster centers and reaches good minima of the objective function. Experimental results carried out on the standard medical datasets like Brain, Lungs, Liver and Breast images. The results show that the proposed approach outperforms many other FCM variants. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.232.188.122

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
S. Harish, B.; V. Aruna Kumar, S.; Masulli, F. and Rovetta, S. (2017). Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-222-6; ISSN 2184-4313, SciTePress, pages 591-598. DOI: 10.5220/0006210905910598

@conference{icpram17,
author={B. {S. Harish}. and S. {V. Aruna Kumar}. and Francesco Masulli. and Stefano Rovetta.},
title={Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2017},
pages={591-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006210905910598},
isbn={978-989-758-222-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images
SN - 978-989-758-222-6
IS - 2184-4313
AU - S. Harish, B.
AU - V. Aruna Kumar, S.
AU - Masulli, F.
AU - Rovetta, S.
PY - 2017
SP - 591
EP - 598
DO - 10.5220/0006210905910598
PB - SciTePress