Particle Analysis Using Improved Adaptive Level Set Method Based Image Segmentation

Particle analysis is one of the most difficult tasks in material science and technology. Detection of size and shape of particles is important for gaining information about the material as well as for better control over the quality of the product. Image processing techniques predominantly segmentation technique provides effective analysis of size and shape features of material particles by segregating contiguous particles which further helps in counting total number of particles in an image. This paper presents an improved adaptive level set segmentation technique by utilizing, an adaptive directional speed, stopping force based on weighted probability and mathematical morphological operations to overcome the disadvantages of false boundary detection and sensitivity to evolution curve's initial position which is present in the traditional level set methods. In this paper, the adaptive level set based image segmentation methodology is applied on different material science laboratory microscopic images in order to effectively achieve parameters such as particle number, area, size, roundness and size distribution, etc.