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Segmentation of cotton leaf images using a modified chan vese method

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Abstract

Segmenting the leaf images from the complex background is the current research topic. So, in this paper, we propose an algorithm to segment the leaf image from the natural background. Diverse methods exist in the literature, like region-based, edge-based, clustered-based, deformable models. Among these, deformable models are more advantageous, which leads to the use of the method for leaf segmentation. There are two types of deformable models, namely geometric and parametric models. The geometric model has many advantages over the parametric model; hence, we present the comparative study of the proposed model and other well-known algorithms. The proposed method combines the chan vese method with the level set method without re-initialization. It also uses bilateral filtering to remove the noise from the image for more vital image information, which helps in the fast evolution process. The main objective of the method is to segment a leaf from natural background. For our study, the model used the cotton leaf database with nearly 300 images. The results show that the proposed model modified chan vese method gives better results than other state-of-the-art performance parameters. The proposed method parameters Precision, Recall, Sensitivity, Specificity, Accuracy., Jaccard Index, F1 score values are 0.9685, 0.9949, 0.9949, 0.9817, 0.8897,0.9388 respectively.

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Authors and Affiliations

  1. School of CSA, REVA University, Bengaluru, India

    Bhagya M. Patil

  2. Department of Information Science & Engineering, PDA College of Engineering, Kalaburgi, India

    Vishwanath Burkpalli

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  1. Bhagya M. Patil
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  2. Vishwanath Burkpalli
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Correspondence to Bhagya M. Patil.

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Patil, B.M., Burkpalli, V. Segmentation of cotton leaf images using a modified chan vese method. Multimed Tools Appl 81, 15419–15437 (2022). https://doi.org/10.1007/s11042-022-12436-8

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