Leaves are one of the important parts in a tree. Extracting accurately the shape of a leaf is a crucial step in image-based systems. The partial or total absence of textures on leaf surface and the high color variability of leaves belonging to the same species make shape as the main recognition element. For such reasons, leaf segmentation plays a decisive role in the leaf extraction process. Even though many general segmentation methods have been proposed in the last decades, leaf segmentation presents specific challenges. In particular, a pixel-level precision is required in order to highlight fine scale boundary structures and discriminate similar global shapes. The authors propose a robust and accurate method for segmenting objects acquired under various controlled conditions. Then, they have improved the performance of the segmentation methods using preprocessing tools such as, color distance map and input strokes. Based on these methods, we can eliminate unwanted boundaries and localize the leaf object efficiently. They have implemented a Hybrid Guided Active Contour (GAC) method to measure geometric properties of leaf images, and have provided with a comparative study for various segmentation algorithms based on performance metrics. Based on experimental results, GAC provides improved performance in leaf datasets.