Comparative Analysis of Chronic Kidney Disease Detection Using K-means and FCM Technique

Comparative Analysis of Chronic Kidney Disease Detection Using K-means and FCM Technique

Swapan SamaddarRamaswamy Reddy

Dept of CSE, VFSTR University, Vadlamudi, Guntur 522213, AP, India

Corresponding Author Email:
11 August 2018
12 September 2018
30 September 2018
| Citation



About these study, we would alike to existent Chronic Kidney Disease revealing procedures, established on the conservative a new Spatial Fuzzy-technique and K-means technique investigation of kidney MRI images. Although, the K-means was previously utilized in kidney MRI segmentation of image, along with segmentation of image at overall, this miss the mark to exploit the robust spatial association amongst neighbouring pixels. A spatial Fuzzy C-means (SFCM’s) procedure, that is exploit the evidence of spatial accurately and generate extraordinary kidney images segmentation. To check the segmentation performance of Spatial Fuzzy C-means and K-means method, we have used 5 ground truth images. The outcomes of segmentation, that are demonstrated extra precise segmentation with the SFCM’s matched with that of K-means are offered statistically and graphically.


SFCM, MRI image, fuzzy, K-means

1. Introduction
2. Materials and Methods
3. Investigational Outcomes
4. Discussions
5. Conclusion

[1] Ahmed S, Iftekharuddin KM. (2011). Efficacy of texture, shape and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE Trans. Inform. Technol. Biomed 15(2): 206-213. 

[2] Yang Y, Huang SY. (2007). Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term. Computing and Informatics 26(1): 17–31.

[3] Vannier MW, Speidel CM, Rickman DL. (1991). Validation of Magnetic Resonance Imaging (MRI) multispectral tissue classification. Comput. Med. Imaging Graph 15(4): 217-223. PMID: 1913572. 

[4] Deighton M, Petrou M. (2003). Supervised segmentation of volume textures using 3D Probabilistic Relaxation. SCIA, LNCS 2749. Springer-Verlag, Berlin, Germany. pp. 869-876. 

[5] Jouvin MH, De Vernejoul MC, Druet P. (1987). Fluoride—induced chronic renal failure. American Journal of Kidney Disorders 10(2): 136–139.

[6] Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ. (2006). Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph 30(1): 9-15.

[7] Saeed M, Karl WC, Rabiee HR, Nguyen TQ. (1998). A new multiresolution algorithm for image segmentation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2753-2756.

[8] Pham DL. (2003). Unsupervised tissue classification in medical images using edge-adaptive clustering. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 634-637.

[9] Christ MCJ, Parvathi RMS. (2011). Fuzzy c-means algorithm for medical image segmentation. Electronics Computer Technology (ICECT). 3rd International Conference on 4: 33-36.

[10] Evangelin J, Suresh P. (2015). Segmentation driven image application to 2D-MRI of kidney. Proceedings of the International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015].

[11]  Mahdi M, Plataniotis KN, Stergiopoulos S. (2017). An automated approach for kidney segmentation in three-dimensional ultrasound images. Proceedings of the IEEE Journal of Biomedical and Health Informatics 21(4): 1079-1094.