Medical Image Segmentation Based on Extreme Learning Machine Algorithm in Kernel Fuzzy C-Means Using Artificial Bee Colony Method

In image segmentation field, the Fuzzy C-Means (FCM) algorithm is a well-known algorithm for its simplicity and membership function that can control the overlapped clusters effectively with a predefined number of clusters. Despite the fact, the standard FCM algorithm is noise sensitive. To solve the issue, we proposed a new method of clustering named Kernel Fuzzy C-means (KFCM) clustering. KFCM performed well in terms of clustering however, for pattern recognition KFCM has issues. The first one is grouping the similar objects in a single partition due to nonawareness of patterns and the second one is misclassification of data due to the standard structure of the membership subspace plane. Non-awareness of patterns of KFCM is solved by an Extreme Learning Machine (ELM) algorithm and Artificial Bee colony (ABC) algorithm utilized for optimizing the structure of the membership subspace plane. Experimental results showed that the proposed KFCM algorithm performed better segmentation for pattern recognition. At last effectiveness of the proposed algorithm has been evaluated based on comparing the K Means, FCM, spatial FCM, and KFCM algorithms in terms of centroids, segmentation accuracy, and pixel error. The proposed methodology improved the segmentation accuracy up to 0.8-5.5% compared to the existing methods.

[1]  Tao Tang,et al.  A Kernel Clustering Algorithm With Fuzzy Factor: Application to SAR Image Segmentation , 2014, IEEE Geoscience and Remote Sensing Letters.

[2]  Getachew Alemu,et al.  Brain tumor detection and segmentation using hybrid intelligent algorithms , 2015, AFRICON 2015.

[3]  Hitesh Gupta,et al.  Image Segmentation using Fuzzy C Means Clustering: A survey , 2013 .

[4]  L. Pan,et al.  A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding , 2004 .

[5]  D. N. F. Awang Iskandar,et al.  Multimodal Brain Tumor Segmentation using Neighboring Image Features , 2017 .

[6]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Francisco de A. T. de Carvalho,et al.  Kernel fuzzy c-means with automatic variable weighting , 2014, Fuzzy Sets Syst..

[8]  Yogita K. Dubey,et al.  Color Image Segmentation Using Kernalized Fuzzy C-means Clustering , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.

[9]  Sim Heng Ong,et al.  Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation , 2011, Comput. Biol. Medicine.

[10]  Haiyang Li,et al.  Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation , 2015 .

[11]  Carlos Alberto Silva,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. , 2016, IEEE transactions on medical imaging.

[12]  Maoguo Gong,et al.  Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation , 2013, Signal Process..

[13]  Yuhui Zheng,et al.  A Novel Brain Tumor Segmentation from Multi-Modality MRI via A Level-Set-Based Model , 2017, J. Signal Process. Syst..

[14]  Cheng Wu,et al.  Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.

[15]  H. N. Suresh,et al.  Image segmentation based on modified centroid weight particle swarm optimization and spatial fuzzy C-means clustering algorithm , 2015, 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).

[16]  Xavier Descombes,et al.  Coastline detection by a Markovian segmentation on SAR images , 1996, Signal Process..

[17]  Licheng Jiao,et al.  Spectral clustering ensemble for image segmentation , 2009, GEC '09.

[18]  Ahmed Atwan,et al.  Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering , 2015 .

[19]  S. R. Kannan,et al.  Effective fuzzy c-means based kernel function in segmenting medical images , 2010, Comput. Biol. Medicine.

[20]  Maoguo Gong,et al.  Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation , 2013, IEEE Transactions on Image Processing.