Fully automatic segmentation method for medicinal plant leaf images in complex background

Vein is a vascular bundle in a leaf which often extends from the leaf center to the edge. In this paper, an accurate and fully automatic segmentation method for medicinal plant leaf images in complex background is proposed by taking vein enhancement and extraction in the image as the core. It has laid a solid foundation for the nondestructive machine identification of medicinal plants taking a mobile phone as the terminal. Gradient magnitude image and gradient angle image are obtained by directly solving color RGB images. By fully applying the objective rule that the veins are linear and the consistency of gradient angles of adjacent vein points is relatively high, the veins in the gradient magnitude images are further enhanced by the standard deviation of the gradient angles to obtain the vein enhancement image. Based on this image, the OTSU method is used to obtain a binary image taking the veins as the foreground, and the main veins are detected from it. In other areas beyond the main veins in the vein enhancement map, fine veins are detected and then connected to the main veins. Then, a foreground marker image targeting the veins is obtained. The background marker image is segmented by using the OTSU method in each component image of the color image and then screened after a specific ratio is calculated. Based on the foreground markers and the background markers, the marker-controlled watershed method is applied to obtain the binary segmentation result. A series of experimental tests based on a self-built database and another widely used database show that the accuracy of the proposed method is better than many main fully automatic image segmentation methods including deep learning FCN. Its running speed is also fast and slightly lower than that of the OTSU threshold segmentation.