Image Segmentation Using Combined Level Set and Morphological Methods

In medical imaging, low contrast is a common issue that makes features difficult to distinguish within images. To address this challenge, we can employ a combined approach using level set methods and morphological operations for image segmentation. The level set method helps identify regional boundaries in images through partial differential equations, while morphological operations (such as dilation, erosion, opening, and closing) further refine the segmentation results by enhancing shape characteristics and removing noise. Implementation typically involves initializing a level set function that evolves according to image gradient information, followed by applying morphological filters to smooth boundaries and fill small gaps. Key functions in MATLAB might include `activecontour` for level set implementation and `imopen`,`imclose` for morphological processing. This combined method effectively improves segmentation performance for low-contrast medical images by leveraging the boundary detection capabilities of level sets and the shape-processing advantages of morphological operations. In practical applications, this methodology has gained widespread adoption in medical image analysis and has demonstrated satisfactory results in segmenting challenging low-contrast anatomical structures.