Comparative Evaluation of Marker-Controlled Method and Gradient Distance Transformation through Watershed Segmentation

Image segmentation is a fundamental process in computer vision and image analysis, with applications in various fields. By dividing digital images into distinct regions or objects based on their characteristics, such as color, intensity, or texture, image segmentation allows for more efficient object recognition, accurate medical image analysis, and improved computer vision algorithms. This paper aims to provide a comprehensive examination of one popular method for image segmentation, the watershed transformation, and discuss its limitations and potential remedies. By understanding the challenges and possible solutions in digital image segmentation, researchers and practitioners can enhance the accuracy and effectiveness of their image analysis techniques.

I. Introduction

Watershed segmentation is a popular image-processing technique used for segmenting objects or regions of interest in an image [4]. It treats the image as a topographic relief, where the pixel intensities represent elevations. The segmentation is performed by identifying the watershed basins corresponding to the regions separated by the watershed lines.