IDENTIFICATION AND CLASSIFICATION OF POWDER MICROSCOPIC IMAGES OF INDIAN HERBAL PLANTS

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International Journal of Electronics and Communication Engineering & Technology
(IJECET)
Volume 7, Issue 3, May–June 2016, pp. 18–28, Article ID: IJECET_07_03_003
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© IAEME Publication
IDENTIFICATION AND CLASSIFICATION
OF POWDER MICROSCOPIC IMAGES OF
INDIAN HERBAL PLANTS
Shraddha Vyas
Department of Computer Engineering, NSIT,
Jetalpur, Ahmedabad, India,
Bhupendra Fataniya
Department of Electronics and Communication, Nirma University,
Ahmedabad, India,
Tanish Zaveri
Department of Electronics and Communication, Nirma University,
Ahmedabad, India
Sanjeev Acharya
Institute of Pharmacy, Nirma University,
Ahmedabad, India,
ABSTRACT
This paper proposes an automated algorithm for plant identification using
microscopic images of powder of herbal plants. In current scenario, the task
of identifying plant from its powder form is done by pharmaceutical
companies, who performs this task manually. This process takes lots of effort
and time. Microscopic images of powder contains varieties of information,
which are important evidence for identification of the plant. With every
image, different type of noise are present, which makes the segmentation as a
critical job. In this paper, we are proposing an algorithm which performs this
task automatically by a computer. The proposed method consists three steps:
Object Detection, Feature Extraction and Classification. Firstly, microscopic
images of "Liquorice" and "Rhubarb" plants were taken. On those images
Top-hat and Bot-hat transformation are performed. Wiener Filter is used for
image smoothing. Image segmentation is performed using Otsu's thresholding
algorithm and find region of interest. The extra blobs were removed using
morphological operations. The feature extraction phase derives features based
on shape of the object. These features are used as an input to the classifier for
efficient classification and the results were tested using SVM classifier. Our

Identification and Classification of Powder Microscopic Images of Indian Herbal Plants
proposed algorithm shows the efficiency for successfully detection of
Liquorice and Rhubarb plants are 88.73% and 92.06% respectively using
SVM classifier.
Key words: Classification, Feature Extraction, Microscopic Images, Plant
Identification, Super vector machine
Cite this Article: Shraddha Vyas, Bhupendra Fataniya, Tanish Zaveri and
Sanjeev Acharya, Identification and Classification of Powder Microscopic
Images of Indian Herbal Plants, International Journal of Electronics and
Communication Engineering & Technology, 7(3), 2016, pp. 18–28.
http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=7&IType=3
1. INTRODUCTION
At present, identification of powder of herbal plant is done using manual microscopy
in pharmaceutical industries. In manual identification of herbal plants, human
expertise is required and also less sample is tested in particular time as compared to
automatic identification. Many pharmaceutical companies have demand the automatic
identification of powder from herbal plants. The main approach of identification
includes physical or chemical monitoring. Specialists are needed for proper
identification. Due to lack of specialists, identification work is very difficult.
Microscopic images of powder were taken, when we performed this task
automatically [1].
This microscopic image contains varieties of information, which are important
evidence for identification of the plant. So, our basic need was to remove un-
necessary blobs from image, which are present with object of interest. Then image
segmentation algorithm was applied to get clear object. The main goal of image
segmentation is to domain independent partitioning of an image into disjoint regions.
The objective of digital image processing is to extract useful information from images
without any human attention.
An automatic segmentation is difficult for machine. Because, every image has
different internal structure, either simple or complex background. So, the
segmentation is the most difficult part of automatic plant identification process [2]. If
object of interest is not perfectly identified, machine classifies it as a different object.
Adulteration and misclassification of herbal drug can cause serious health problems to
consumers, as well as publicity and legal headaches to pharmaceutical companies [1].
This paper describes our approach for the plant identification using powder
microscopic images of herbal plants. The block-diagram of proposed algorithm is
shown in Fig 1. In microscopy, plant identification is based on its characteristics like
plant's trichome and crystal structure. The performance of this experiment is evaluated
using Liquorice and Rhubarb herbal plants. Their microscopic images are shown in
Fig 2. (a) and (b) respectively.
Slides of plants in powder form are prepared with chemical and images are
captured by zooming at different scale in Laboratory. With 4x images, object and
background are looking same. So object is not visible correctly. Object and
background are separately visible With 10x images. So, with different scale images,
complexity is different. We face some problems like high number of unnecessary
blobs present in image as shown in Fig 2 (a). Different images have different
orientation of same object, which adds difficulties in image segmentation and feature
extraction.

Shraddha Vyas, Bhupendra Fataniya, Tanish Zaveri and Sanjeev Acharya
For these kind of images, if we have directly applied any segmentation algorithm,
we will not get appropriate segmented image. To reduce this complexity, we are using
combination of Top-hat and Bot-hat transformation as pre-processing steps in
proposed algorithm. Then we applied Otsu's segmentation algorithm. The present
results with original images, segmented images and images after morphological
operations are satisfactory remove the noise from images. The feature extraction
phase derives feature based on the shape of object like Compactness factor,
Roughness factor and Fourier descriptor. These features are used as an inputs to the
classifier for efficient classification and the results were tested using SVM classifier.
This paper is organized as follows. In section 2, Proposed Algorithm is explained
with block diagram. Pre-Processing steps of proposed algorithm are explained in
details with resulting images. In section 3, contains analysis of shape factors and
classification using SVM classifier. In section 4, obtained results are presented and
discussed. Section 5 draws the conclusion and gives some final remarks.
2. PROPOSED ALGORITHM
In this paper, our aim is to propose a new method of automatic object detection and
classification for powder microscopic images. We have taken two herbal plants of
Liquorice and Rhubarb. The main feature used for detection of Liquorice plant is
large stonecells with larger area [3]. The stonecells lie hidden under the cell array and
are clearly visible when the image is converted to a binary image. The characteristic
features of Rhubarb is small in size and has a darker area at the center. Rhubarb
crystal is in circular shape [4]. Fig 1 depicts the steps involved in typical image
analysis workflow showing segmentation as key step for succeeding image
representation and recognition stages.
Figure 1. Block diagram of Proposed System
Input Image: Images of Liquorice and Rhubarb plants in powder form are taken.
These images are shown in Figure 2. (a) and (b) respectively. The proposed system is

tested on dataset of Liquorice and Rhubarb plants, which contains 714 images of these
two plants. Each image in dataset is of 300×300 resolution having white background
and black object.
Pre-Processing: In order to extract any specific information, pre-processing steps are
carried out before the actual analysis of the image data. Pre-processing refers to the
initial processing of input image to eliminate the noise and correct the distorted data.
Figure 2 illustrate the techniques like grayscale conversion, top-hat and bot-hat
transformation, filtering, segmentation and object detection. Thus, the sequential steps
of Otsu's method for segmenting the object from its background can be summarized
as follows.
RGB to Grayscale Conversion. For many applications in image processing, color
image does not help to identify important edges or other features. for that reason,
grayscale conversion is necessary.
Apply Top-hat transform. Structuring element of disk type is created with the radius
of 15. Top-hat transform on grayscale image using structuring element generates
morphological opened image (erosion followed by dilation) from original image [10-
12].
Apply Bot-hat transform. Applying Bot-hat transform using same structuring
element subtracts original image from morphological closed image (dilation followed
by erosion). Result of this transform shows the gaps between the objects. To
maximize the contrast between the objects and the gaps that separate them from each
other, the example adds the top-hat image to the original image, and then subtracts the
"bottom-hat" image from the result [10-12].
Apply Wiener Filter. Wiener filter works adaptively on image. It tailoring itself to
the local image variance. Where the variance is large, it performs less smoothing.
Where the variance is small, it performs more smoothing.
Apply Otsu's Image Segmentation Algorithm [5]. Otsu's thresholding method
involves iterating through all the possible threshold values and calculating measure of
spread for the pixel level of each side of the threshold. It is clustering method based
on minimizing between class variance [6]. In Otsu's method, we determine threshold
that lies between the range of 0 and 1. It gives good performance on images with
variable illumination.
(a) (b)

(c) (d)
(e) (f)
(g) (h)
(i) (j)
(k) (l)
Figure 2. (a) and (b) Original images of Liquorice and Rhubarb respectively, (c) and (d)
Grayscale images, (e) and (f) Images after subtraction of Bot-hat transform from addition of
Original image and Top-hat transformed image, (g) and (h) Images after applying Wiener
Filter, (i) and (j) Images after applying Otsu's segmentation algorithm, (k) and (l) Images
after removing extra blobs around an object of interest and then apply complement to get
white object and black background.
Object Detection: After performing Otsu's thresholding method, we have obtained
above images in which some blobs were still present around the object of interest.
Morphological operation eliminates blobs smaller than specified area in the field.
Then filling the area inside an object. Apply complement to get object with black
background and white object.

3. FEATURE EXTRACTION
Feature extraction [7] techniques are applied to get features that will be useful in
classifying and recognition of images. Feature extraction describes the relevant shape
information contained in a pattern, so that the task of classifying the pattern is made
easy. The main goal of feature extraction is to obtain the most relevant information
from the original data and represent that information in lower dimensionally space.
Shape Factors: Shape is a fundamental property of an object. Shape features are
dimensionless quantities used in image analysis and microscopy that numerically
describe the shape of a particle. Shape features are calculated from measured
dimensions such as: diameter, area, perimeter.
For feature extraction we have selected three shape factors that depends on shape
characteristic. These are: Compactness Factor, Moments, Fourier Descriptor [8].
Compactness Factor: It is a measure of efficiency of a contour to contain a given
area. It is applicable to all geometric shapes. It is independent of shape and
orientation. It is defined by:
= /A (1)
where P is contour perimeter and A is area enclosed. The value of compactness is
larger when the area contained by contour of given length is smaller. The definition of
compactness is modified to restrict the range of parameter to interval [0,1] as
1-4 / (2)
Roughness Factor: This shape factor is defined with moments and describes
roughness. One definition of moments is based upon a sequence d(n) that represents
the Euclidean distances between the centre of mass and all of the points along the
contour. The moment of the sequence d(n) is defined as
= (3)
and the central moment is defined as
= (4)
where p is the order of moments.
To form a set of shape features, selected following normalized low order
moments.
= / (5)
= / (6)
= / (7)
where m1 is the first moment, and M2, M3 and M4 are the second-, the third- and
the forth-order central moment, respectively. The variations in F2 and F3 for differing
shape complexity are small and do not show a simple progression.
In order to overcome these limitations F2 and F3 were modified as follows
= / (8)
= / (9)

As the contour becomes rougher, the fourth-order term in F3' becomes much
larger than the second-order term in F1, and a good indicator of shape roughness is
the following measure
f= - (10)
Measure mf provides the desired invariance for a given contour type as well as the
desired variation across the various shape categories.
Fourier descriptor Is a method used in object recognition to represent the boundary
shape of a segment in an image. With Fourier descriptors, global shape features are
captured by the first few low frequency terms, while higher frequency terms capture
finer features of the shape. Given a sequence z (n), it is possible to derive Fourier
descriptors of the contour as
Z (k)= exp[ ] (11)
where k = -N/2, ..., -1, 0, 1, ..., (N/2)-1. The normalized Fourier descriptors Zo(k)
are defined as
(k) = (12)
so that magnitudes are independent of position, size, orientation and starting point
of the contour. A shape factor ff based upon the normalized Fourier descriptors was
defined as
ff=1- (13)
The advantage of this measure is that it is limited to the range [0,1] and is not
sensitive to noise.
Computation of shape factors: The first step in the shape analysis was an adequate
representation of the contour in the form that is suitable for the calculation of shape
measures. Therefore, the contour was recorded as a series of coordinates that
represents points of contours. The starting point was located left of the centre of the
mass and movement was in clockwise direction. Centre of the mass was calculated as
mean value of all coordinate points that belong to the contour.
After determining the signature of the contour and the centre of the mass, they
were used for calculating shape factors. Shape factors were calculated for all contours
of training set. Table1 shows the values of shape factors for different contours.
Table 1. Values of Shape Factors
Binary image Type
Shape Factors
Cf Mf ff
Liquorice 0.9116 0.1510 0.9998
Liquorice 0.9157 0.0750 0.9994

Rhubarb 0.8901 0.0388 0.9979
Rhubarb 0.8619 0.0338 0.9983
Classification: In our research work, we have used Support Vector Machine for data
classification using Compactness factor, Roughness factor and Fourier Descriptor
features.
Support Vector Machine (SVM) [9] is one of the best known methods in pattern
classification and image classification. SVM classifies data by finding the best hyper
plane that separates all points of one class from those of other class. Support vectors
are the data points that are closest to the separating hyper plane. SVM is designed to
separate of a set of images into different classes (x1, y1), (x2, y2), (xn, yn) where xi in
Rd, d-dimensional feature space, and yi in {-1,+1}, the class label, with i=1..n. SVM
builds the optimal separating hyper planes based on a kernel function.
When points are separated by non-linear region, we need a non-linear dividing
line. This is done by kernel function to map the data into a different space where
hyper plane can be used to do the separation. All images, of which feature vector lies
on one side of the hyper plane, are belong to class -1 and the others are belong to class
+1.The choice of a Kernel depends on the problem at hand because it depends on
what we are trying to model. A polynomial kernel, for example, allows us to model
feature conjunctions up to the order of the polynomial. Radial basis functions allow to
pick out circles - in contrast with the Linear kernel, which allows only to pick out
lines (or hyperplanes).
To classify our data using SVM, we have used different kernel functions (Linear,
Rbf, Sigmoid and Polynomial) to separate data into different categories by drawing
non-linear hyperplane and made a comparison table of their results.
4. RESULTS
Segmentation Results using Proposed Method: Different experiments were
conducted using the images of Liquorice and Rhubarb herbal plants. Results are
displayed in Fig. 3. In results, we have mentioned 3 images within each row:- input
image, image after Otsu's threshold and final segmented image. We can see that
orientation of object is different and object is scaled at different position in every
image. With different input image, lots of blobs are present with interested object.
Some blobs were still present with Otsu's thresholding image. They were removed
by morphological operations by keeping the constant threshold for all Liquorice
images as 2700 and for all Rhubarb images as 250. So we got perfect segmented
image.
Classification using SVM: Input vector is in combination of (cf, mf ; mf, ff ; cf, ff).
For all classifiers, X is matrix of predictor data, where each row is one observation,
and each column is one predictor. and Y is an array of class labels with each row
corresponding to the value of the corresponding row in X. Y can be a character array,
categorical, logical or numeric vector, or vector cell array of strings.
After selecting the data for classification, trained SVM classifier with different
kernel functions. SVM classifier is trained using input vector X as a 2 column matrix

and array of classification labels Y with different kernel functions. Input vector is
taken in combination of cf, mf ; mf, ff and cf, ff.
Different Kernel Functions( Linear, Rbf, Sigmoid and Polynomial ) are taken for
train data and separate into different categories by drawing non-linear hyperplane.
Then predict classification using trained SVM model and test dataset, which
generates Output labels. By using Target labels and Output labels, confusion matrix is
plotted. The goal was to find the kernel function that gives highest accuracy of results.
Then calculated above two parameters (cross validation and kfoldLoss) for all
combination of input vectors and generate confusion matrix using predicted labels and
target labels.
We have taken 70% of data for training and 30% of data for testing. After trained
data using SVM classifier, cross validate classifier using trained classifier. Then cross
validation loss of model is measured using kfoldLoss. Table 2 shows classification
accuracy of different combination of input vectors for different Kernel Functions and
Table 3 shows Misclassification using SVM Classifier.
Table 2. Classification accuracy of different combination of input vectors for different kernel
functions
Parameters Linear RBF Sigmoid Polynomial
Cf, Mf 83.53% 80.85% 57.46% 80.84%
Mf, Ff 84.28% 92.06% 85.92% 90.15%
Cf, Ff 77.02% 68.61% 77.02% 70.32%
Table 3. Misclassification Table of Liquorice and Rhubarb Plants using SVM Classifiers
Parameters Linear RBF Sigmoid Polynomial
Cf, Mf 17.60% 9.80% 16.40% 9.80%
Mf, Ff 18.40% 14.20% 18.40% 14.60%
Cf, Ff 28.20% 19.20% 27.00% 24.40%
Results of Table 2 shows that, the combination of moments and Fourier descriptor
correctly classifies Liquorice and Rhubarb 92.06% cases with kernel function "RBF".

(a) (b) (c)
Figure 3. Shows images of Liquorice and Rhubarb plant respectively. From these images, 1st
three are images of Liquorice plant and remaining two images of rhubarb plant. (a) Shows
input images of Liquorice and Rhubarb plants respectively. (b) Shows images after Otsu's
segmentation algorithm on Liquorice and Rhubarb plants. (c) Shows images of final detected
object.
5. CONCLUSION
An automatic image segmentation algorithm for microscopic images of herbal plants
in powder form has been proposed in the present paper. Using our proposed method,
we got 88.73% efficiency for Liquorice and 92.06% accuracy for Rhubarb. Our
Proposed algorithm is not working for images with multiple objects overlapping on
each other. Morphological operations fill inside area of total connected components.
So machine understands it as a single object.

REFERENCES
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Electron Microscopy in the Identification of Herbal Medicines'', Microscopy:
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(Eds.).
[2] Jun Li, Yixu Song, Yaoli Li, Shaoqin Cai, Zehong Yang, "Automatic Target
Segmentation based on Texture for Microscopic Images of Chinese Herbal
Powder", Control and Decision Conference (CCDC), pp.1473-1478, at Guiyang,
May 2013.
[3] "Quality Standards of Indian Medicinal Plants", Vol 9.
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[6] Sheenam Bansal, Raman Maini, "Comparative Analysis of Iterative and Otsu's
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