Open AccessUtilizing k-means clustering to extract bone tumor in CT scan and MRI images
Author(s) -
Widad Dhahir Kadhim,
Rabab Saadoon Abdoon
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1591/1/012010
Subject(s) - cluster analysis , segmentation , magnetic resonance imaging , dilation (metric space) , artificial intelligence , pixel , computer science , computed tomography , image segmentation , computer vision , nuclear medicine , pattern recognition (psychology) , radiology , medicine , mathematics , combinatorics
Segmentation is one of the most significant parts of medical image processing. In image segmentation, the digital image is part of multiple sets of pixels. Magnetic Resonance Imaging, MRI and CT scanning is very important imaging techniques to explore the inner physiological constructions of the body noninvasively. A bone tumor is one of more life-threatening diseases, so exact detaching of the tumor regions is a pressing need. In this work, the K-means algorithm is employed on six MRI and CT scan images with different numbers of clusters. As well as many morphological operations like opening and dilation were applied after extract the fine tumor areas effectively. The results and the calculated surface areas of the separated tumor regions were compared to the radiologist delineation and the percent relative differences were found ranged from (0.63-1.75) % for MRI images and (0.34-1.51) % for CT scan images. This result indicates the high-quality performance of the adopted segmentation clustering-based method.