Abstract
The segmentation of images is the most essential and basic component of image evaluation and healthcare systems. In image analysis, this is the most difficult process since it determines the efficacy of the results. It is difficult to automatically segment ultrasound images when speckle noise and artefacts are present, which are key components of other medical imaging. Segmentation strategies will vary depending on the level of segmentation and the amount of information needed. In this work, a Spatial Fuzzy C-Mean clustering approach is utilized for segmenting the ultrasound image of the foetal. Foetal images are given as an input to clustering algorithm, which generates feature vectors for each pixel. In clustering, the foetal image is divided into parts based on spatialization. Image quality is improved by applying an anisotropic diffusion filter before segmentation. Based on the results of the experiments, the Spatial Fuzzy C-Means clustering approach yields promising outcomes.
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Eveline Pregitha, R., Vinod Kumar, R.S., Ebbie Selva Kumar, C. (2024). Spatial Fuzzy C-Mean Clustering Method for the Segmentation of Ultrasound Foetal Images. In: Bhateja, V., Chowdary, P.S.R., Flores-Fuentes, W., Urooj, S., Sankar Dhar, R. (eds) Evolution in Signal Processing and Telecommunication Networks. ICMEET 2023. Lecture Notes in Electrical Engineering, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-97-0644-0_33
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DOI: https://doi.org/10.1007/978-981-97-0644-0_33
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