Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing

At present, the region growing algorithm has been used as a segmentation technique of digital images. Most region growing algorithms are using fixed or determinate criterions to distinguish disease spots from leaf image with gray level differences between leaf and disease spot. But in practice, the objects in the disease leaf image have fuzziness and uncertainty, and edges of the objects are unclear. What´s more, the color of leaf and disease spots is uneven, and the gray level is overlapping, so it is difficult to use fixed threshold or determinate criteria to determine the uncertain objects in leaf disease spot images accurately. In order to improve the crop leaf spot disease image segmentation accuracy, an adaptive segmentation algorithm by integrating local threshold and seeded region growing (LTSRG) is proposed. The algorithm was implemented on VC6.0. The segmentation algorithm uses the pixels of which the R-channel gray level is more than the G-channel gray level as initial seed points (pixels), and then local threshold C_i is calculated for each connected seed region by Otsu. New seed pixels are included and the threshold C is re-calculated until no new seed pixel can be included. The results of LTSRG are compared with the results of threshold-based Otsu and clustering -based EM. The experiments show: The adapted segmentation method is satisfactory and highly efficient to separate disease spots from normal part of corn leaves. LTSRG algorithm is easy to realize, and can improve the precision of crop disease spot segmentation. Its image segmentation results have good region consistency and high efficiency. It is an adapting algorithm for image segmentation.