Effect Of Noise over MRI Segmentation Techniques
| Author(s) | : | Syed Mujtiba Hussain, Shivani Gadhi, Nusrat Ara |
| Institution | : | Department of Computer Science and Engineering,Islamic University of Science and Technology. |
| Published In | : | Vol. 5, Issue 13 — January 2018 |
| Page No. | : | - |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
this approach is basically used to compare the extracted patterns of internal body tissues which were affectedby various noises during acquisition. Segmentation is a computational intelligence discipline which has emerged as avaluable tool for disease analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled datafrom the MRI image can be clustered first and after that segmentation can be applied in order to obtain the pattern oroutlook of a particular organ or tissue so that diagnosticians can use them for diagnosing and finally analyzing thetissues. There are various algorithms which are used to solve this problem. In this paper two important segmentationalgorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means) clusteringalgorithms are compared. These algorithms are applied to the MRI image of thoracic cavity and performance isevaluated on the basis of the efficiency they provide when they are affected by various types of noises.
Syed Mujtiba Hussain, Shivani Gadhi, Nusrat Ara, “Effect Of Noise over MRI Segmentation Techniques”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 13, pp. -, January 2018.
