Abstract
Automated segmentation of abnormal medical images using computing algorithms is a challenging task. Among other segmentation and clustering algorithm, Fuzzy C Means (FCM) is beneficial for producing accurate results. In this paper, the empirical work concentrated on Identification of tumor from spinal cord MRI by determining the accuracy of the affected region on FCM cluster result with different filtering techniques. At first, Linear Support Vector Machine (SVM) is used to classify the image as normal or abnormal. Once the anomaly confirmed MRI images are pre-processed with different filters such as Arithmetic, Gaussian, Median, Wiener and Anisotropic diffusion; for the enhancement without changing the details of the image. Each Filtering has unique characteristic over the dataset. All the pre-processing data is clustered using FCM to identify the tumor region. The best filtering technique suitable for the clustering is selected based on the accuracy and processing time taken on various numbers of clusters. The proposed algorithm-anisotropic diffusion with FCM’s performance measures gave an efficient result.
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Shyni Carmel Mary, S., Sasikala, S. (2020). A Decision Support System for MRI Spinal Cord Tumor Detection. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_40
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DOI: https://doi.org/10.1007/978-981-15-1420-3_40
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