Iran J Public Health, Vol. 45, No.10, Oct 2016, pp.1276-1287 Original Article
1276 Available at: http://ijph.tums.ac.ir
A Bio Medical Waste Identification and Classification Algorithm
Using Mltrp and Rvm
*Aravindan ACHUTHAN
1
, Vasumathi AYYALLU MADANGOPAL
2
1. Dept. of Civil Engineering, Latha Mathavan Engineering College, Madurai, Tamil Nadu, India
2. Dept. of Civil Engineering, Sethu Institute of Technology, Kariyapatti, Tamil Nadu, India
*Corresponding Author: Email: aaravindancivil@gmail.com
(Received 22 Mar 2016; accepted 10 Aug 2016)
Introduction
Bio Medical Waste (BMW) originates from hu-
man, animal health care, medical teaching facili-
tates, medical research, biological laboratory
waste and other facilities. A part of that waste
stream is infectious or potentially infectious and
it must be appropriately managed to defend the
sanitation and healthcare personnel. Normally,
the BMWs (1-3) are regulated and managed ac-
cording to various standards and protocols in
different countries. In health care facilities, the
wastes are generated during improper manage-
ment, which causes a direct health impact in the
community, the environment and the health care
workers. BMW is a dangerous health hazard to
the public, hospital, health care units, flora and
fauna of the area. BMW must be stored in a se-
cure environment at all times, whenever possible,
BMW should not be mixed with chemical, radio-
active or other laboratory trash. Containers for
BMW must be appropriate for its contents, there
are different kinds of containers, and bags are
available for the containment and disposal of
BMW (4). The Government of India specifies
that BMW is a part of hospital hygiene and
maintenance activities. The World Health Organ-
ization (WHO) has categorized the BMW into
eight categories, includes,
General Waste
Infectious or dangerous waste
Abstract
Background:
We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical
Waste (BMW) for garbage disposal and management.
Methods:
The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise
in the image. After that, the histogram features of the filtered image were extracted with the help of proposed Modi-
fied Local Tetra Pattern (MLTrP) technique. Finally, the Relevance Vector Machine (RVM) was used to classify the
BMW into human body parts, plastics, cotton and liquids.
Results: The BMW image was collected from the garbage image dataset for analysis. The performance of the pro-
posed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate
and accuracy with the help of MATLAB. When compared to the existing techniques, the proposed techniques provid-
ed the better results.
Conclusion: This work proposes a new texture analysis and classification technique for BMW management and dis-
posal. It can be used in many real time applications such as hospital and healthcare management systems for proper
BMW disposal.
Keywords: Bio medical waste, Median filter, Sensitivity, Specificity
Achuthan and Ayyallu Madangopal: A Bio Medical Waste Identification and Classification Algorithm …
Available at: http://ijph.tums.ac.ir 1277
Radioactive
Chemical
Pathological
Pressurized containers
Pharmaceuticals
Preprocessing is an essential step in image pro-
cessing applications, which eliminates the irrele-
vant noises in the given input image. Median fil-
ters are widely used in many images processing
application, because it provides excellent noise
reduction capabilities for noise removal. In this
paper, an improved median filtering technique is
applied to remove the noises in the given BMW
image. After denoising, the texture features of the
preprocessed BMW image is extracted based on
the histogram value by using the MLTrP.
In this research, the type of BMW is identified
and classified with the help of MLTrP and the
RVM classifier. Image processing algorithms ap-
ply local and global operations on an input image
for some particular reasons, such as, noise elimi-
nation, edge detection and contrast stretching.
The Local Binary Pattern (LBP), Local Derivative
Pattern (LDP) and Local Tetra Pattern (LTP) are
the existing feature extraction approaches. These
techniques are mainly used to extract the infor-
mation based on the distribution of edges, coded
using only two directions such as, positive and
negative directions. The performance of these
methods can be enhanced by differentiating the
edges in more than two directions. In order to
overcome this limitation, the MLTrP extraction
method is used in this work. The MLTrP is a
three direction code that illustrates the spatial
structure of the local texture by using the direc-
tion of the central gray pixel. BMW should be
classified according to their source, type and risk
factors (5). In this paper, MLTrP for BMW clas-
sification is proposed based on a diagonal, hori-
zontal and vertical direction. The text on is an
essential concept in texture analysis, applied to
develop effectual models in texture recognition.
Moreover, the extraction of pattern is used to
classify each pixel using tetra direction and the
extraction of magnitude pattern is collected using
the magnitude of derivatives. There are different
classification techniques are available for an effi-
cient image classification. Some of the existing
classification techniques compared in this paper
is, Artificial Neural Network (ANN) (6), decision
tree (7), Support Vector Machine (SVM) (8) and
fuzzy measure (9). ANN is a non-parametric uni-
versal classifier and it efficiently handles the noise
input. The disadvantages of this technique are, its
computational rate is high, it is semantically poor,
it has the over fitting problem and it consumes
more time for training. Decision tree is a non-
parametric training data that provides hierarchical
associations between input variables to forecast
class membership. It is simple and the computa-
tional efficiency is good, which are major ad-
vantages of this technique. It becomes more
complex, when various values are correlated that
is the main limitation of this approach.
SVM is an unsupervised learning classifier that
gains the flexibility in the form of threshold and
contains a non-linear transformation. The ad-
vantages of this method are, it has lesser compu-
tational complexity and it is simple to manage the
decision rule complexity. The disadvantages of
this technique are, the resultant transparency of
SVM is low, it consumes less time for training,
the structure of this technique is not easy to un-
derstand and the determination of optimal pa-
rameters are is more complex. In order to over-
whelm these drawbacks, the RVM classification
technique is used in this paper.
After extracting the texture features, the RVM
classifier is used to classify the BMW wastages.
RVM is a prevalent choice for classification task,
which provides more advantages over SVM. In
this analysis, it is mainly used for the classifica-
tion of input BMW image. After classification,
the types of wastages are identified in order to
segregate and dispose the BMW properly. In this
analysis, four types of wastages are identified and
classified such as, human body parts, plastics,
cotton and liquids. The main intention of this
work was to define BMW and to provide infor-
mation about the identification and classification
of this waste stream. For this purpose, the
MLTrP with RVM classification method is pro-
posed in this paper.
Iran J Public Health, Vol. 45, No.10, Oct 2016, pp.1276-1287
1278 Available at: http://ijph.tums.ac.ir
We aimed to extract the histogram features for
text analysis and, to classify the types of BMW
for garbage disposal and management.
Methods
Here, the MLTrP and RVM were used to effi-
ciently extract and classify the histogram features
of BMW image. The main aim of this work was
to extract the histogram features from the BMW
image and to classify the type of BMW as plastic,
cotton, liquid or human body parts. The pro-
posed BMW image classification system was de-
signed at the year of 2014 using the MATLAB
tool with certain Garbage images. This paper in-
troduced a new framework for proper garbage
disposal and management.
Preprocessing
Preprocessing images generally involves eliminat-
ing background noise, stabilizing the intensity of
the specific particle images, eliminating reflec-
tions and masking portions of images. Image
preprocessing is the method of increasing data,
images proceeding to computational processing.
The preprocessing of BMW at the source of gen-
eration is the initial step, but is an essential step
in health care waste management. In this re-
search, the median filter is used to preprocess the
BMW image. Median filtering is a nonlinear
method, mainly used to remove the noise from
images. It is very effective and extensively used
technique in noise removing while preserving
edges. Median filter is one of the main building
blocks in image processing applications, which
adequately removes the salt and pepper type of
noises. It removes the noises in the BMW image
by moving through the image pixel by pixel. The
pattern of neighbors is referred as window, which
slides pixel by pixel over the entire BMW image.
Initially, the median is calculated by arranging all
pixel values from the window into numerical or-
der. After that, it replaces each value as the medi-
an value of the neighboring pixels. Fig. 1 shows
the process of median filtering based BMW im-
age preprocessing.
Fig. 1: Process of Median filtering
Initially, the BMW image is given to the input
and the noises present in this image is removed
by using the median filtering technique. Then, the
texture features of the denoised image are ex-
tracted by using the MLTrP technique. In this
stage, the patterns are extracted and the encoded-
Achuthan and Ayyallu Madangopal: A Bio Medical Waste Identification and Classification Algorithm …
Available at: http://ijph.tums.ac.ir 1279
magnitude patterns are calculated based on the
pixel directions (0°, 45° and 90°). Hence, the his-
togram features are selected and given to the in-
put of RVM classifier. Finally, it classifies the
BMW image into human body parts, cotton, plas-
tics and liquid wastages.
Feature Extraction
Feature extraction is the process of capturing the
visual content of images for indexing and retriev-
al. Feature extraction involves facilitating the
amount of resources needed to represent a large
set of data exactly. The aim of feature extraction
is to represent the raw image in its reduced form
to facilitate decision-making process such as pat-
tern classification. Feature extraction is an essen-
tial step to get high classification rate. A set of
features are extracted in order to allow a classifier
to identify the normal and abnormal pattern. The
BMW image region is determined by the way of
gray levels are distributed over the pixels in the
region. The properties of the BMW image region
are quantified by exploiting space relations under-
lying the gray level distribution. In this paper, the
features of the input BMW image are extracted
by using the MLTrP. Fig. 2 shows the overall
flow of the proposed MLTrP based feature ex-
traction and RVM based classification.
Fig. 2: Overall flow of the proposed BMW identification and classification
Iran J Public Health, Vol. 45, No.10, Oct 2016, pp.1276-1287
1280 Available at: http://ijph.tums.ac.ir
MLTrP
In the field of texture feature extraction and clas-
sification, the MLTrP is a stepping stone. It is
mainly used to build the relationship between the
central pixel and the neighboring pixels by calcu-
lating the gray level difference. The MLTrP en-
codes the input BMW image by calculating the
horizontal and vertical directions of each pixel. It
codes the relationship based on the direction of
the central pixel p
c
and neighbors. The directions
are calculated by combining (n-1)th – order deriv-
atives of the 0°, 45° and 90°directions. The first
order derivatives along 0°, 45° and 90° directions
are denoted as,
Let
denotes the central pixel in image D,
indicates the horizontal neighborhood,
indi-
cates the vertical neighborhood and
repre-
sents the diagonal neighborhoods of
respec-
tively. Then the first order derivatives at the cen-
ter pixel is calculated as,
[1]
[2]
[3]
The center pixel’s direction is calculated as,
[4]
From equation [4], it is apparent that the possible
direction for each center pixel can be 1, 2, 3, 4, 5,
6, 7 or 8 and eventually, the image is converted
into eight directions. The sec order
is defined as follows,
[5]
[6]
The 8-bit tetra pattern for each central pixel is
obtained from equations [5] and [6]. Then, all
patterns are separated into 8 parts based on the
direction of center pixel. Where, the direction of
the center pixel
is obtained from
equation [4]. Hence,
[7]
[8]
Where, similarly, the other
tetra patterns for remaining 8 directions of center
pixels are converted to binary patterns. Finally, 56
(8 7) binary patterns are taken. Fig. 3 shows the
process of histogram feature extraction with the
help of MLTrP.