Clustering is a suitable procedure in which objects are clustered, depends up on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity levels. The different clustering approaches are used for the cluster creation with better performance. In this research work, it is analysed lung cancer dataset by the two major clustering algorithms namely k-Means and Fuzzy C-Means (FCM).They are implemented in segmentation phase in this research work and also compared the execution time of these two algorithms. Implementation is established on the aspect of class wise cluster building capability of algorithms using lung CT DICOM images. The expansion of DICOM file is Digital Imaging and Communications in Medicine. This implementation mainly focused on the analysis of real time data in the medical domain. The input images are pre-processed using filters and then evaluated. The clustering techniques are applied to identify the center point and intensity ranges for pixels based on input dataset via CTDICOM images of lung. The performance of these two algorithms is reported finally as per the results produced by these algorithms. The final findings and outcome of this research work is used for convenient decision making in the department of oncology in order to detect the cancer affected regions.
Volume 11 | 09-Special Issue
Pages: 494-502
DOI: 10.5373/JARDCS/V11/20192597