Li Hua, Chen Chuanbo, Zhao Shengrong, Lyu Zehua
School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
School of Information, Qilu University of Technology, Jinan 250353, Shandong, China.
Technol Health Care. 2018;26(S1):151-156. doi: 10.3233/THC-174525.
Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research.
This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease.
First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced.
Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge.
The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area.
彩色图像中的病叶分割用于探究病害形状和病斑区域。这对病理诊断和病理研究具有重要意义。
本文提出一种使用非对称与方形反堆积模型（NAMS）的超像素算法用于病叶的彩色图像分割。
首先，提出NAMS模型用于彩色病叶图像表示。该模型能够非对称地分割图像并保留图像上下文特征。其次，提出基于NAMS的超像素（NAMS超像素）算法用于像素聚类，其能够用超方形表示大的同质区域。通过这种方式，可以减少复杂背景和图像分割中数据冗余的影响。
实验结果表明，与直接分割原始图像以及通过简单线性迭代聚类（SLIC）超像素进行处理相比，所提出的NAMS超像素在节省存储以及贴合病斑区域边缘方面表现更出色。
NAMS超像素的结果可被视为病叶区域检测的预处理步骤，因为该方法能够将图像分割成超像素块并保留病斑区域。
