Malaria is an serious infectious disease which is
mainly diagnosed by visual microscopical evaluation of Giemsa
stained blood smears. As it poses a serious global health problem,
automation of the evaluation process is of high importance. The
propose a set of features for distinguishing between non-infected
red blood cells and cells infected by malaria parasites and
evaluate the performance of these features on the set of red blood
cells from the created database. The developed graphical user
interface provides all tools necessary for creating a database of
red blood cells. This approach proved to deliver good results on
images with various qualitative characteristics resulting in only
occasional over-segmented cells. The main part of this work is
devoted to the extraction of features from the red blood cell
images that could be used for distinguishing between infected
and non-infected red blood cells. We propose a set of features
based on shape, intensity, and texture and evaluate the
performance of these features on the red blood cell samples from
the created database using receiver operating characteristics. The
results have shown that some of the features could be
successfully used for malaria detection.
Ashok M. Sutkar : Walchand Collage of Engineering, An Autonomous Institute, E & TC, Maharashtra, India
Marathe N. V. : Walchand Collage of Engineering, An Autonomous Institute, E & TC, Maharashtra, India
RBC Component
Microscopic images
Parasites
Feature Extraction
NN Classifier
SVM Classifier
ANFIS
classifier
This project addresses how the identification of malaria
diseases is possible using image processing by effectively
analyzing various parameter of blood cell image by using GLCM as Energy and other like Skewness, Kurtosis, and
Standard Deviation. The experimental results indicate that
the proposed approach is a valuable approach, which can
be significantly support an accurate identification of
malaria diseases in a little computational effort. There can
be mistake in counting manually the number of RBC &
WBC (process of Giemsa) as the boundaries are not
clearly defined or visible which lead us to the error in
wrong decision. So to solve this problem the developed
algorithm be more helpful the other techniques. As this
system can meet the real time application requirements, so
we can easily have the standalone working version of this
system. Support vector machine gives good accuracy as
compared to neural network.
[1] Yashasvi Purwar, Sirish L Shah, Gwen Clarke, Areej
Almugairi and Atis Muehlenbachs, “Automated and
unsupervised detection of malarial parasites in
microscopic images”, Purwar et al. Malaria Journal 2011,
10:3641,Department of Chemical and Materials
Engineering, University of Alberta, Edmonton, Canada.
[2] Pallavi T. Suradkar, “Detection of Malarial Parasite in
Blood Using Image Processing”, International Journal of
Engineering and Innovative Technology (IJEIT) Volume
2, Issue 10, April 2013A.
[3] Sriram R., Meenalochani Chandar and Kota Srinivas,
“Computer Aided Malarial Diagnosis for JSB Stained
White Light Images Using Neural Networks", International
Journal of Advanced Research in Computer
Science and Software Engi-neering, Volume 3, Issue 8,
August 2013, CSIR-CSIO, Chennai, India.
[4] S.Abdul Nasir, M.Y.Mashor and Z.Mohamed,
“Segmentation Based Approach for Detection of Malaria
Parasites Using Moving K- Means Clustering”,
Department of Medical Microbiology & Parasitology,
School of Medical Sciences, Health Campus, Universiti
Sains Malaysia, IEEE EMBS International Conference
on Biomedical Engineering and Sciences, Langkawi, 17th
- 19th December 2012.
[5] Saowaluck Kaewkamnerd, Apichart Intarapanich, Montri
Pannarat, Sastra Chaotheing, Chairat Uthaipibull and
Sissades Tongsima, “Detection and Classification Device
for Malaria Parasites in Thick- Blood Films” The 6th
IEEE International Conference on Intelligent Data
Acquisition and Advanced Computing Systems:
Technology and Applications 15-17 September 2011,
Prague, Czech Republic.
[6] S.Kareem, I.Kale and R.C.S Morling, “Automated
Malaria Parasite Detection in Thin Blood Films:- A
Hybrid Illumination and Color Constancy Insensitive,
Morphological Approach”, IEEE Trans.on Applied DSP
and VLSI Research Group University of Westminster
London, United Kingdom, 2012.
[7] Isha Suwalka, Ankit Sanadhya, Anirudh Mathur and
Mahendra S Chouhan, “Identify Malaria Parasite Using
Pattern Recognition Technique” Geetanjali Institute of
Technical Studies Dabok, Udaipur, India,2011. [8] D. Anggraini, A. S. Nugroho, C. Pratama, et al.,
“Automated status identification of microscopic images
obtained from malaria thin blood smears,” in 2011
International Conference on Electrical Engineering and
Informatics (ICEEI), Bandung, Indonesia, 2011.
[9] Matthias Elter, Erik Haßlmeyer and Thorsten Zerfaß
Fraunhofer, “Detection of malaria parasites in thick
blood films”, Institute for Integrated Circuits IIS Image
Processing and Medical Engineering Department Am
Wolfsmantel 33, 91058 Erlangen, Germany, 33rd Annual
International Conference of the IEEE EMBS Boston,
Massachusetts USA, August 30 - September 3, 2011.
[10] Amit Kumar, Manjunatha M, J Chatterjee and Ajoy K
Ray, “Segmentation of Blood Smear Images using
Normalized Cuts for Detection of Malarial Parasites”
Dept. of Electronics & Electrical Communication
Engineering, Annual IEEE India Conference (INDICON)
2010.A. A. Name, and B. B. Name, Book Title, Place:
Press, Year.
