Particle method for segmentation of breast tumors in ultrasound images

Abstract We propose a new segmentation method based on multiple walking particles (WP) bouncing from the image edges. The particles are able to segment objects characterized by deep concavities as narrow as one pixel and handle single or multiple objects characterized by a noisy background and broken boundaries (“weak edge”, “boundary leakage”). The particles are designed to segment the image by permanently staying inside the object and repairing the boundaries where necessary. The proposed WP combine the advantages of the continuous diffusion models with the principles of multi-agent systems. WP have been tested against recent active contours and the distance regularized level set method on a set of complex-shaped synthetic images and ultrasound (US) images of breast cancer ( http://onlinemedicalimages.com ). The method has also been compared with localizing region-based active contours, the fuzzy C-mean level set method, and morphological active contours. The WP are faster and more accurate for images characterized by low contrast, noise, broken boundaries, or boundary leakage. However, for good quality, simple shaped objects the WP work similarly to the conventional methods. There is still an important difference even in this case: the WP do not require initialization. A video demo of the algorithm is at https://drive.google.com/drive/folders/1SlTphINKtdUwvdjjxiakFrwI2dDlIUAU