|
Differentiation of metastatic and primary brain tumor using magnetic resonance imaging
|
M. Yasir   , F. Siddique , F. Andleeb , G. Gilanie , H. Ullah |
|
|
|
Abstract: (35 Views) |
Background: Brain tumor segmentation is an important aspect of medical image processing. Timely detection of brain tumors can increase a patient's survival rate significantly. Doctors use traditional manual techniques for diagnosis, but these techniques are tedious and require intricate programming. Materials and Methods: New developments of automatic and semi-automatic approaches for image processing have been applied. MRI images are characterized using segmentation and registration Toolkit (ITK-SNAP) software to find and demarcate boundaries and assess brain tumor volume from MRI images. This study included six untreated brain tumor patients (aged 35-75) to explore the feasibility of using semi-automated MRI techniques for tumor segmentation. Despite the small sample size, the method showed promising accuracy, highlighting its potential for clinical use. Results: Our findings revealed accurate diagnosis of primary brain tumors, 3D tumor size, tumor edges, and metastatic brain tumors based on MRI images. ITK-SNAP proved to be more effective for diagnosing metastatic and secondary brain tumors than conventional techniques. The importance of effective and accurate segmentation, particularly discrimination among primary and metastatic tumors, was highlighted. Conclusion: On the contrary, completely automatic approaches tend to be imprecise. Tailoring the ITK-SNAP tool for brain tumor segmentation maximizes efficiency with improved accuracy for differentiating tumor categories. |
|
| Keywords: Primary brain tumors, metastatic brain tumors, MRI, semi-automated segmentation, computer-assisted image interpretation. |
|
|
Full-Text [PDF 1210 kb]
(4 Downloads)
|
|
Type of Study: Original Research |
Subject:
Radiation Biology
|
|
|
|
|
|
|
| Add your comments about this article |
|
|
|
