Lung Image Segmentation Using K-means Clustering Algorithm with Novel Distance Metric

Content Provider	Semantic Scholar
Author	Saraswathi Sheela, T.
Copyright Year	2016
Abstract	Clustering is one of the most important data mining techniques that can handle unlabeled data. K-means is a distance-based clustering algorithm. K-means groups the data objects into K disjoint clusters. K is a user specified parameter. Since, K-means is a simple algorithm it has been used in a wide variety of applications as well as in image processing. .Its implementation is very simple and fast execution. The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering. In this paper the K-Means clustering algorithm with various distance metrics are applied on images and the performance is analyzed. The various distance functions such as Euclidean distance, Manhattan distance, Chebyshev distance, Minkowski distance and Sorensen distance functions are used. In addition this paper proposes a pioneer distance metric with KMeans clustering algorithm to find better clusters for image segmentation. The work is implemented using MATLAB. The results are compared with the existing techniques by using various performance measures such as Precision Rate, Recall Rate, Sensitivity, Specificity and F-Measure. Experimental results indicate that the proposed distance metric with KMeans algorithm performs better in terms of performance evaluation metrics than the other distance metric functions.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ijrter.com/papers/volume-2/issue-12/lung-image-segmentation-using-k-means-clustering-algorithm-with-novel-distance-metric.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article