fuzzy c means based liver ct image segmentation with optimum number of clusters - srge

Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of

Clusters

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Abder-Rahman Ali

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, June 23-25, 2014

Scientific Research Group in Egyptwww.egyptscience.net

Overview

Motivation Proposed Approach Optimal number of clusters Average Generalized Silhoeutte Results

Conclusion

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Motivation

We investigate the effect of using an optimum number of clusters with Fuzzy C-

Means clustering, for Liver CT image segmentation

Is the optimum always the better?The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Proposed Approach

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Optimal Number of Clusters

Generalized Intra-Inter Silhouettes

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Optimal Number of Clusters (cont…)

Generalized Intra-Inter Silhouettes

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Optimal Number of Clusters (cont...)

Generalized Intra-Inter Silhouettes

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Compactness distance

Optimal Number of Clusters (cont...)

Generalized Intra-Inter Silhouettes

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Separation distance

Optimal Number of Clusters (cont...)

Generalized Intra-Inter Silhouettes

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

silhouette

[-1, +1]

If bi-aj +: good clustering

If bi-aj - : poor clustering

Average Generalized Silhouette

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

• Average Generalized is considered in this work, since we are interested in the overall clustering quality of the entire dataset

• Average Generalized Silhouette returns a vector of silhouette values, one value for each data point (pixel)

• If one point has a silhouette value near 1, then its clustering is very good

Average Generalized Silhouette (cont…)

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

• If the silhouette is near -1, then the clustering of the point is very bad

• A silhouette value of 0 indicates an intermediate case

• Each silhouette is considered a measure of the clustering quality of the associated point

Results

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

The first and second images, the best number of clusters to be used is 3. For the third image, the best number of clusters to be used is 4. And, for the fourth and fifth images, the best number of clusters to be used is 2

Results (cont…)

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

where the optimal number of clusters to be used are larger than 2-clusters, based on Table (1) in the previous slide, they gave the best Jaccard index values. And, where the optimal number of clusters to be used are 2-clusters, choosing a random number of clusters in the range 3-5 gave better Jaccard index values

Results (cont…)

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

• The figure represents image (1) from the table

• Using 3-clusters, as recommended by the average silhouette value, shows more clearly the groundtruth than using 2-clusters

Conclusions

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

Choosing the correct number of clusters is very important in Fuzzy C-Means clustering

it was noticed that it is not always necessary that using the optimum number of clusters with FCM, as measured by the average silhouette value, always gives the best results in terms of Jaccard index

Thanks and Acknowledgement

The 5th International Conference on Innovations in Bio-Inspired Computing and Applications. June 23-25, 2014

http://www.egyptscience.net

Authors: Abder-Rahman Ali, Micael Couceiro, Aboul Ella Hassenian, Mohamed F. Tolba5, and Vaclav Snasel