Wheat leaf lesion color image segmentation with improved multichannel selection based on the Chan-Vese model

- An improved Chan-Vese(C-V) model for wheat leaf lesion image segmentation is proposed.
- Methods of adaptive channel selection and weight computation are proposed.
- The termination criterion greatly reduces the iterations in the level set evolution.
- Our method shows superior performance of accuracy, efficiency and robustness over others.

Because of the characteristics of intensity inhomogeneity, noise, and blurred edges in crop lesion color images, an improved Chan-Vese (C-V) model for wheat leaf lesion segmentation is proposed. First, to make full use of the color information, three color channels are selected from the R, G, B, H, S, and V channels using principal component analysis. In addition, an initial K-means segmentation is used to obtain the initial lesion curve. Second, because the channel weights are artificially determined in the C-V model, the ratio of the average object pixel value to average background pixel value is used as the adaptive weight for each of the three channels. Finally, to avoid a long iteration process for the level-set evolution, an efficient termination criterion is presented. The proposed algorithm has the advantages of an adaptive channel selection, adaptive channel weight computation, and very few iterations. Compared with the traditional C-V and gradient descent C-V (g-CV) models, the proposed segmentation approach has fewer iterations and higher segmentation accuracy.