Title:Prediction of Tetralogy of Fallot using Fuzzy Clustering
Volume: 13
Issue: 4
Author(s): K.R. Kosala Devi*V. Deepa
Affiliation:
- Department of Computer Science and Engineering, Vel Tech Rangarajan R&D Institute of Science and Technology, Chennai,India
Keywords:
Edge detection, Gray scale conversion, Clustering Techniques, GLCM, FELICM, Canny Edge Detection.
Abstract:
Background: Congenital Heart Disease is one of the abnormalities in your heart's structure.
To predict the tetralogy of fallot in a heart is a difficult task. Cluster is the collection of data objects,
which are similar to one another within the same group and are different from the objects in the other
clusters. To detect the edges, the clustering mechanism improve its accuracy by using segmentation,
Colour space conversion of an image implemented in Fuzzy c-Means with Edge and Local Information.
Objective: To predict the tetralogy of fallot in a heart, the clustering mechanism is used. Fuzzy c-Means
with Edge and Local Information gives an accuracy to detect the edges of a fallot to identify the congential
heart disease in an efficient way.
Methods: One of the finest image clustering methods, called as Fuzzy c-Means with Edge and Local Information
which will introduce the weights for a pixel value to increase the edge detection accuracy value.
It will identify the pixel value within its local neighbor windows to improve the exactness. For evaluation
, the Adjusted rand index metrics used to achieve the accurate measurement.
Results: The cluster metrics Adjusted rand index and jaccard index are used to evaluate the Fuzzy c-
Means with Edge and Local Information. It gives an accurate results to identify the edges. By evaluating
the clustering technique, the Adjusted Rand index, jaccard index gives the accurate values of 0.2,
0.6363, and 0.8333 compared to other clustering methods.
Conclusion: Tetralogy of fallot accurately identified and gives the better performance to detect the edges.
And also it will be useful to identify more defects in various heart diseases in a accurate manner.
Fuzzy c-Means with Edge and Local Information and Gray level Co-occurrence matrix are more promising
than other Clustering Techniques.
