A Novel Fuzzy C—Mean Based Segmentation Technique for Spinal Cord Tumors from MR Images
Keywords:
Brain tumor, image segmentation, Fuzzy C Means algorithm, Magnetic Resonance Image.Abstract
Image processing plays a crucial role in extracting meaningful information from images to enhance their utility and effectiveness. Among various techniques, image segmentation stands out as an efficient method for extracting and isolating specific features within images. This research focuses on optimizing the Fuzzy C Means (FCM) algorithm for accurately identifying the axial and coronal planes in MRI brain images, considering both the algorithm's accuracy and computational efficiency.
The preprocessing phase involves converting MRI brain images from DICOM format to a standard image format. To enhance image quality, a Gaussian filter technique is applied to eliminate noise. Subsequently, the FCM algorithm is implemented to segment regions affected by brain tumours in MR images. The evaluation of algorithmic efficiency and accuracy involves comparing histogram values of images before and after segmentation with the cluster center values determined by the FCM algorithm.
The results provide insights into the algorithm's performance, with a focus on computational time as a key metric. By identifying the best fit of the FCM algorithm for both axial and coronal planes, this research contributes to advancing the field of image segmentation in the context of brain tumor detection. In conclusion, the study underscores the significance of FCM algorithm in accurately delineating tumor-affected regions in MRI brain images, thereby aiding in the diagnosis and treatment of brain tumors. The identified optimal parameters showcase the potential of FCM as a valuable tool in the realm of medical image analysis.
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