Neutrosophic and fuzzy C-means clustering for breast ultrasound image segmentation

Breast ultrasound image segmentation is one of the most difficult tasks due to its speckle noise, poor quality and location of the breast nodule. In this research, we propose normalisation algorithm to enhance image contrast in order to be segmented using neutrosophic and fuzzy c-means clustering. At first, the input image is filtered using speckle reduction anisotropic diffusion to reduce speckle noise and normalised to increase the contrast. Secondly, the normalised image is transferred to neutrosophic domain with three membership subset T, I and F to define the nodule area. Finally, the fuzzy c- mean method is used to segment the nodule area from the background. To evaluate and compare the performance of the proposed method, this research uses several measurements, namely Area Metric and Boundary Metric. The result shows that implementation of normalisation improves the performance of segmentation results.