CN105761259A - Wheat leaf stoma density measurement method based on microscopic image - Google Patents

Wheat leaf stoma density measurement method based on microscopic image Download PDF

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CN105761259A
CN105761259A CN201610085012.8A CN201610085012A CN105761259A CN 105761259 A CN105761259 A CN 105761259A CN 201610085012 A CN201610085012 A CN 201610085012A CN 105761259 A CN105761259 A CN 105761259A
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stomata
wheat leaf
wheat
stomatal
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CN105761259B (en
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李林
左志宇
魏新华
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Jiangsu University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G06T2207/10061—Microscopic image from scanning electron microscope
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture

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Abstract

本发明公开了一种基于显微图像的小麦叶片气孔密度测量方法，属于计算机视觉技术领域。采用数字显微镜采集小麦叶片图像，采用混合的灰度化方法对采集的图像进行灰度化，得到小麦叶片的灰度图像；采用最大类间方差法自适应进行图像二值化处理；根据小麦叶片气孔排列方向自适应选取线性结构元素进行形态学开运算，消除背景噪声的影响；进行连通区域检测，得到图像中小麦叶片气孔的位置信息，统计视场内气孔数量，根据显微镜标定的尺寸进行气孔密度计算。本发明能够实时对显微图像中小麦叶片气孔的密度进行检测，有效解决了现有体系中人工计算时的费时费力问题，可应用于不同胁迫或不同生长环境下小麦叶片气孔特性的影响研究以及小麦生长模型的研究。

The invention discloses a method for measuring the stomatal density of wheat leaves based on microscopic images, which belongs to the technical field of computer vision. A digital microscope is used to collect wheat leaf images, and a mixed grayscale method is used to grayscale the collected images to obtain a grayscale image of wheat leaves; the maximum between-class variance method is used to adaptively perform image binarization; according to the wheat leaf The stomatal arrangement direction adaptively selects linear structural elements for morphological opening operations to eliminate the influence of background noise; detects connected areas to obtain the position information of the stomata of wheat leaves in the image, counts the number of stomata in the field of view, and calculates the stomata according to the size calibrated by the microscope Density calculations. The invention can detect the stomatal density of wheat leaves in microscopic images in real time, effectively solves the time-consuming and labor-intensive problem of manual calculation in the existing system, and can be applied to the research on the influence of stomatal characteristics of wheat leaves under different stresses or different growth environments and Wheat growth model studies.

Description

A kind of wheat leaf blade stomatal frequency measuring method based on micro-image
Technical field
The invention belongs to technical field of computer vision, be specifically related to a kind of wheat leaf blade stomatal frequency measuring method based on micro-image.
Background technology
In recent years, the pore character of crop has become the important research content of the basic research such as crop yield physiology and stress physiology.The density of pore can affect photosynthesis and the transpiration of plant, is one of important parameter characterizing wheat leaf blade stomatal properties.Major part research at present is all the measurement being carried out stomatal frequency by artificial counting, the method not only lavishes labor on, and when data volume is very big, is manually easy to because mistake occurs in fatigue, also can be subject to the impact of outside environmental elements, it is impossible in time the data gathered are processed.Along with the development of computer technology, image processing techniques, due to its environmental protection, not by advantages such as external environment are limited, is widely used in multiple links of agriculture field, including the visual system of the detection of agricultural product, crop growth conditions monitoring and agricultural robot.
Summary of the invention
It is an object of the invention to provide a kind of method of wheat leaf blade stomatal frequency in automatic calculating visual field.Processed by the wheat leaf blade image that digital microscope is collected, obtain the quantity of wheat leaf blade pore in visual field, thus calculating the density obtaining wheat leaf blade pore.In order to realize above-mentioned target, the present invention mainly adopts the following technical scheme that:
A kind of wheat leaf blade stomatal frequency measuring method based on micro-image, carries out as steps described below:
(1) image acquisition and gray processing process
Using digital microscope to gather the image of wheat leaf blade pore, computer reads the image gathered and processes.Consider that in the coloured image of wheat leaf blade, G component is higher, in order to isolate air cap preferably, the image gathered is carried out gray processing by the gray processing method adopting mixing, then by regular for gray value be double (double-precision floating point type) type in 0 to 1 scope, obtain the gray level image of wheat leaf blade.
(2) image segmentation
Adopt maximum variance between clusters that the image of gray processing is carried out binary conversion treatment, obtain image segmentation result.Maximum variance between clusters divides the image into background and target two parts by the gamma characteristic of image, inter-class variance between background and target is more big, illustrating that the two-part difference of pie graph picture is more big, therefore, the segmentation making inter-class variance maximum means that misclassification probability is minimum.
(3) image denoising
Choose linear structure element according to wheat leaf blade pore orientation self adaptation and carry out morphology opening operation, eliminate the impact of background noise.The arrangement of wheat leaf blade pore aligns mutually, and the process of adaptive detection pore orientation is as follows:
A. arranging horizontal direction is 0 degree of angular direction, and respectively to 0 °, 45 ° clockwise, 90 ° clockwise, 135 ° of four directions clockwise carry out row gray average statistics, obtain the gray average variance in each direction, the maximum direction of variance be image differentiate direction in advance；
B. carry out the opening operation of the linear structure element vertical with differentiating direction in advance according to the other direction of anticipation, eliminate tiny effect of noise；
C. direction is the image of 0 ° and 45 ° clockwise, seeks the abscissa average of the element that its each column is not zero, and carries out fitting a straight line, obtains the direction of accurate wheat leaf blade pore.Direction is the image of 90 ° and 135 ° clockwise, seeks the abscissa average of the element that its each row is not zero, and carries out fitting a straight line, obtains the direction of accurate wheat leaf blade pore；
D. carry out overall situation straight-line detection according to the direction of accurate wheat leaf blade pore, obtain meeting the parameter of all straight lines of wheat leaf blade pore orientation.
(4) connected region detection
Image segmentation result is carried out connected region detection, all connected regions detected are detected, be think hole area in fact when it meets stomatal parameters, obtain the positional information of Semen Tritici aestivi Stoma of Leaves in image.The parameter of pore includes the distance etc. of area, length-width ratio and the straight line from its nearest neighbours.
(5) statistics connected region quantity, obtains stomatal frequency
Finally statistics visual field inner air vent quantity, the actual size according to the Size calculation visual field that microscope is demarcated, obtain the stomatal frequency of reality.
The outstanding feature of the present invention is the process and the analysis that adopt the method for image procossing to replace manually carrying out micro-image.Image processing process is considered the impact of noise and background, the impact of most noise and background can be removed according to the method for the orientation of detection wheat leaf blade pore.The method that the present invention proposes has good robustness, and the accuracy that the micro-image gathered carries out density measure is higher.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention.
Fig. 2 is the schematic diagram differentiating direction in advance.
Fig. 3 is the angle detecting process of wheat leaf blade pore arrangement.
Fig. 4 is the interpretation of result of the wheat leaf blade stomatal frequency that the present invention obtains.
Detailed description of the invention
The implementation process of the present invention is described in detail below in conjunction with embodiment and accompanying drawing.As shown in Figure 1, a kind of wheat leaf blade stomatal frequency measuring method based on micro-image, carry out as steps described below:
(1) read the image of digital microscope collection and carry out gray processing process
Consider that in the coloured image of wheat leaf blade, G component (green component) is higher, in order to isolate air cap preferably, the image gathered is carried out gray processing by the gray processing method adopting mixing, then by regular for gray value be double type in 0 to 1 scope, obtain the gray level image of wheat leaf blade.Fig. 4 (b) gives the design sketch obtained after the coloured image of collection carries out gray processing, Fig. 4 (a) is the design sketch that ordinary gamma method obtains, it can be seen that Fig. 4 (b) has targets improvement effect in contrast, it is easier to target is split.
The mathematical formulae that gray processing processes is as follows:
f ( x , y ) = 0 R < T 1 , B < T 2 R R &GreaterEqual; T 1 , B < T 2 B R < T 1 , B &GreaterEqual; T 2 R + B R &GreaterEqual; T 1 , B &GreaterEqual; T 2
Wherein, T1And T2For the threshold value of segmentation, the red component of R, G, B respectively coloured image, green component and blue component.
(2) image segmentation
Adopt maximum variance between clusters that the image of gray processing is carried out binary conversion treatment, obtain image segmentation result.Maximum variance between clusters divides the image into background and target two parts by the gamma characteristic of image, inter-class variance between background and target is more big, illustrating that the two-part difference of pie graph picture is more big, therefore, the segmentation making inter-class variance maximum means that misclassification probability is minimum.
(3) image denoising
Choose linear structure element according to wheat leaf blade pore orientation self adaptation and carry out morphology opening operation, eliminate the impact of background noise.The arrangement of wheat leaf blade pore aligns mutually, and the process of adaptive detection pore orientation is as follows:
A. arranging horizontal direction is 0 degree of angular direction, and respectively to 0 °, 45 ° clockwise, 90 ° clockwise, 135 ° of four directions clockwise carry out row gray average statistics, obtain the gray average variance in each direction, the maximum direction of variance be image differentiate direction in advance.Accompanying drawing 2 is pre-differentiation direction schematic diagram, a1、a2、a3、a4Represent the direction of 0 °, 45 ° clockwise, 90 ° and 135 ° clockwise clockwise respectively；
B. carrying out the opening operation of the linear structure element vertical with differentiating direction in advance according to the other direction of anticipation, eliminate tiny effect of noise, Fig. 4 (c) gives the design sketch obtained after embodiment Fig. 4 (b) carries out morphology operations；
C. direction is the image of 0 ° and 45 ° clockwise, seeks the abscissa average of the element that its each column is not zero, and carries out fitting a straight line, obtains the direction of accurate wheat leaf blade pore.Direction is the image of 90 ° and 135 ° clockwise, seeks the abscissa average of the element that its each row is not zero, and carries out fitting a straight line, obtains the direction of accurate wheat leaf blade pore；
D. carrying out overall situation straight-line detection according to the direction of accurate wheat leaf blade pore, obtain meeting the parameter of all straight lines of wheat leaf blade pore orientation, in Fig. 3, black line is the design sketch that this embodiment carries out straight-line detection.
(4) connected region detection
Image segmentation result is carried out connected region detection, all connected regions detected are detected, be think hole area in fact when it meets stomatal parameters, obtain the positional information of Semen Tritici aestivi Stoma of Leaves in image.The parameter of pore includes the distance etc. of area, length-width ratio and the straight line from its nearest neighbours.Fig. 4 (d) gives the result of embodiment Fig. 4 (b) connected region detection, and black rectangle frame is the minimum enclosed rectangle of the connected region detected.
(5) statistics connected region quantity, obtains stomatal frequency
Finally statistics visual field inner air vent quantity, the actual size according to the Size calculation visual field that microscope is demarcated, obtain the stomatal frequency of reality, wherein the formula of stomatal frequency is as follows:

Claims (1)

1.一种基于显微图像的小麦叶片气孔密度测量方法，其特征在于按照下述步骤进行：1. a method for measuring the stomatal density of wheat leaf based on microscopic images, is characterized in that it is carried out according to the following steps: （1）图像采集以及灰度化处理(1) Image acquisition and grayscale processing 使用数字显微镜采集小麦叶片气孔的图像，电脑读取采集的图像并进行处理；考虑到小麦叶片的彩色图像中G分量较高，为了较好的分离出气孔部分，采用混合的灰度化方法对采集的图像进行灰度化，然后将灰度值规整为0到1范围内的double（双精度浮点型）类型，得到小麦叶片的灰度图像；A digital microscope is used to collect images of the stomata of wheat leaves, and the collected images are read and processed by a computer. Considering that the G component in the color image of wheat leaves is relatively high, in order to better separate the stomata, a mixed grayscale method is used to image the stomata. The collected image is grayscaled, and then the grayscale value is regularized into a double (double-precision floating-point) type in the range of 0 to 1 to obtain a grayscale image of the wheat leaf; （2）图像分割(2) Image segmentation 采用最大类间方差法对已经灰度化的图像进行二值化处理，得到图像分割结果；最大类间方差法按图像的灰度特性将图像分成背景和目标两部分，背景和目标之间的类间方差越大，说明构成图像的两部分的差别越大，因此，使类间方差最大的分割意味着错分概率最小；The maximum between-class variance method is used to binarize the grayscaled image to obtain the image segmentation result; the maximum between-class variance method divides the image into two parts, the background and the target, according to the gray-scale characteristics of the image, and the distance between the background and the target The larger the variance between classes, the greater the difference between the two parts that make up the image, so the segmentation that maximizes the variance between classes means the smallest probability of misclassification; （3）图像去噪(3) Image denoising 根据小麦叶片气孔排列方向自适应选取线性结构元素进行形态学开运算，消除背景噪声的影响；小麦叶片气孔的排列相对整齐，自适应的检测气孔排列方向的过程如下：According to the arrangement direction of wheat leaf stomata, linear structural elements are adaptively selected for morphological opening operation to eliminate the influence of background noise; the arrangement of wheat leaf stomata is relatively neat, and the process of adaptively detecting the arrangement direction of stomata is as follows: A、设置水平方向为0度角方向，分别对0°，顺时针45°，顺时针90°，顺时针135°四个方向进行列灰度均值统计，得到每个方向的灰度均值方差，方差最大的方向为图像的预判别方向；A. Set the horizontal direction as the direction of 0 degree angle, and perform column gray mean value statistics for the four directions of 0°, 45° clockwise, 90° clockwise, and 135° clockwise respectively, and obtain the gray mean variance of each direction. The direction with the largest variance is the pre-discrimination direction of the image; B、根据预判别的方向进行与预判别方向垂直的线性结构元素的开运算，消除细小噪声的影响；B. According to the direction of pre-discrimination, the opening operation of linear structural elements perpendicular to the direction of pre-discrimination is performed to eliminate the influence of small noises; C、方向为0°和顺时针45°的图像，求其每列不为零的元素的横坐标均值，并进行直线拟合，得到精确的小麦叶片气孔的方向；方向为90°和顺时针135°的图像，求其每行不为零的元素的横坐标均值，并进行直线拟合，得到精确的小麦叶片气孔的方向；C, the direction is 0 ° and the image of clockwise 45 °, find the abscissa mean value of the elements that are not zero in each column, and perform straight line fitting to obtain the accurate direction of wheat leaf stomata; the direction is 90 ° and clockwise 135 ° image, find the mean value of the abscissa of the elements in each row that is not zero, and perform straight line fitting to obtain the precise direction of the stomata of the wheat leaf; D、根据精确的小麦叶片气孔的方向进行全局直线检测，得到符合小麦叶片气孔排列方向的所有直线的参数；D, carry out global straight line detection according to the direction of accurate wheat leaf stomata, obtain the parameter of all straight lines that meet wheat leaf stomata arrangement direction; （4）连通区域检测(4) Connected region detection 对图像分割结果进行连通区域检测，对检测到的所有连通区域进行检测，当其满足气孔参数是认为其实气孔区域，得到图像中小麦叶片气孔的位置信息；气孔的参数包括面积、长宽比、与距离其最近的直线的距离等；Carry out connected area detection on the image segmentation results, and detect all the detected connected areas. When it meets the stomatal parameters, it is considered as the stomatal area, and the position information of the wheat leaf stomata in the image is obtained; the parameters of the stomata include area, aspect ratio, The distance from the nearest straight line, etc.; （5）统计连通区域数量，得到气孔密度(5) Count the number of connected regions to get the pore density 最后统计视场内气孔数量，根据显微镜标定的尺寸计算视场的实际尺寸，得到实际的气孔密度。Finally, count the number of pores in the field of view, calculate the actual size of the field of view according to the size calibrated by the microscope, and obtain the actual density of pores.
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