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

Abstract 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 non-destructive 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.

[1]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..

[2]  Cem Kalyoncu,et al.  Geometric leaf classification , 2015, Comput. Vis. Image Underst..

[3]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Aimen Aakif,et al.  Automatic classification of plants based on their leaves , 2015 .

[5]  H. S. Nagendraswamy,et al.  Classification of medicinal plants: An approach using modified LBP with symbolic representation , 2016, Neurocomputing.

[6]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[7]  Camille Couprie,et al.  Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Pablo M. Granitto,et al.  Multiscale recognition of legume varieties based on leaf venation images , 2014, Expert Syst. Appl..

[11]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[12]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Nicolas Passat,et al.  Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology , 2012, Pattern Recognit..

[14]  Shuxiao Li,et al.  Adaptive pyramid mean shift for global real-time visual tracking , 2010, Image Vis. Comput..

[15]  Pablo M. Granitto,et al.  Automatic classification of legumes using leaf vein image features , 2014, Pattern Recognit..

[16]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Serge Beucher,et al.  The Morphological Approach to Segmentation: The Watershed Transformation , 2018, Mathematical Morphology in Image Processing.

[18]  Laure Tougne,et al.  Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods , 2015, IEEE Transactions on Image Processing.

[19]  Xiaohua Lin,et al.  A method for accurately segmenting images of medicinal plant leaves with complex backgrounds , 2018, Comput. Electron. Agric..