TY - GEN
T1 - An improved xie-beni index for cluster validity measure
AU - Singh, Munendra
AU - Bhattacharjee, Romel
AU - Sharma, Neeraj
AU - Verma, Ashish
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - The pathology may appear as a new cluster(s) on radiological images and hence the information of cluster location cannot decide in prior. In this regard, the unsupervised methods of segmentation play the important role, however, these methods need the number of clusters as the input. The challenging tasks in clustering based image segmentation are to choose the number of segments in an image. This work proposes the segmentation quality index, which utilizes the trend of Xie-Beni index to obtain the optimum number of segments in an image. The proposed algorithm has been implemented on the segmentation results obtained by enhanced fuzzy c-means algorithm and compared with the classical validity indexes such as Xie-Beni index, partition entropy coefficient, partition coefficient and fuzzy hyper-volume on synthetic images and simulated brain MRI dataset images. The quantitative results show that the proposed method has greater ability to find the appropriate number of clusters on the ground truth and noisy images.
AB - The pathology may appear as a new cluster(s) on radiological images and hence the information of cluster location cannot decide in prior. In this regard, the unsupervised methods of segmentation play the important role, however, these methods need the number of clusters as the input. The challenging tasks in clustering based image segmentation are to choose the number of segments in an image. This work proposes the segmentation quality index, which utilizes the trend of Xie-Beni index to obtain the optimum number of segments in an image. The proposed algorithm has been implemented on the segmentation results obtained by enhanced fuzzy c-means algorithm and compared with the classical validity indexes such as Xie-Beni index, partition entropy coefficient, partition coefficient and fuzzy hyper-volume on synthetic images and simulated brain MRI dataset images. The quantitative results show that the proposed method has greater ability to find the appropriate number of clusters on the ground truth and noisy images.
UR - http://www.scopus.com/inward/record.url?scp=85046976401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046976401&partnerID=8YFLogxK
U2 - 10.1109/ICIIP.2017.8313691
DO - 10.1109/ICIIP.2017.8313691
M3 - Conference contribution
AN - SCOPUS:85046976401
T3 - 2017 4th International Conference on Image Information Processing, ICIIP 2017
SP - 95
EP - 99
BT - 2017 4th International Conference on Image Information Processing, ICIIP 2017
A2 - Tyagi, Vipin
A2 - Ghrera, Satya Prakash
A2 - Singh, Amit Kumar
A2 - Gupta, P. K.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Image Information Processing, ICIIP 2017
Y2 - 21 December 2017 through 23 December 2017
ER -