Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears

Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears

Md. Jaffar Sadiq

Contact

Dr. V.V.S.S.S. Balaram

Contact

α Jawaharlal Nehru Technological University, Hyderabad

Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears

Article Fingerprint

ReserarchID

CSTGVHMQR3

Review of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

In order to diagnose malaria, the test that has traditionally been conducted is the gold standard test. The process mainly entails the preparation of a blood smear on glass slide, staining the blood and examining the blood through the use of a microscope so as to observe parasite genus plasmodium. Although these are several other kinds of diagnostic test solutions that are available and which can be adopted, there are numerous shortcomings which are always observed when microscopic analysis is carried out. Presently, the treatments are hugely conducted based on symptoms and upon the occurrence of false negatives, it might be fatal and may result into the creation of different kinds of implications. There have been a number of deaths which have been associated with malaria and as a result, there is the dire need to ensure that there is early detection of malarial infection among the people.

References

37 Cites in Article

Reference Format

Jaime Castellot,Jaime García,Bello (2008). ADT-3D Tumor Detection Assistant in 3D.
Cecilia Di Ruberto,Andrew Dempster,Shahid Khan,Bill Jarra (2001). Morphological Image Processing for Evaluating Malaria Disease.
F Tek,A Dempster,I Kale (2006). Malaria Parasite Detection in Peripheral Blood Images.
F Tek,Andrew Dempster,İzzet Kale (2010). Parasite detection and identification for automated thin blood film malaria diagnosis.
Nicholas Ross,Charles Pritchard,David Rubin,Adriano Dusé (2006). Automated image processing method for the diagnosis and classification of malaria on thin blood smears.
Gloria Díaz,Fabio González,Eduardo Romero (2009). A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images.
Vít Špringl (2009). Automatic malaria diagnosis through microscopy imaging.
Maombi Edison,J Jeeva,Megha Singh (2011). Digital analysis of changes by Plasmodium vivax malaria in erythrocytes.
Mohammad Khan,Bhibhudendra Imroze,Bikesh Acharya,Jigyasa Singh,Soni (2011). Content based image retrieval approaches for detection of malarial parasite in blood images.
S Savkare,S Narote (2011). Automated system for malaria parasite identification.
Saravana Kumarasamy,S Kumar,Kevin Sw Ong,Tan (2011). Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection.
Keerthana Prasad,Jan Winter,M Udayakrishna,Raviraja Bhat,Gopalakrishna Acharya,Prabhu (2012). Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.
Maitreya Maity,Ashok K. Maity,Pranab K. Dutta,Chandan Chakraborty (2012). A Web-accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images.
Manuel Cesario,Michael Lundon,Saturnino Luz,Masood Masoodian,Bill Rogers (2012). Mobile support for diagnosis of communicable diseases in remote locations.
Neetu Ahirwar,Sapnojit Pattnaik,Bibhudendra Acharya (2012). Advanced image analysis based system for automatic detection and classification of malarial parasite in blood images.
Ms Suryawanshi,V Snehal,Dixit (2013). Improved technique for detection of malaria parasites within the blood cell images.
Madhumala Ghosh,Devkumar Das,Chandan Chakraborty,Ajoy Kumar,Ray (2013). Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach.
Dev Das,Madhumala Ghosh,Mallika Pal,Asok Maiti,Chandan Chakraborty (2013). Machine learning approach for automated screening of malaria parasite using light microscopic images.
Leila Malihi,Karim Ansari-Asl,Abdolamirbehbahani (2013). Malaria parasite detection in giemsa-stained blood cell images.
J Vink,M Laubscher,R Vlutters,R Kamolratsilamut,M Maude,G Hasan,Haan (2013). An automatic vision-based malaria diagnosis system.
A Nithyaa,R Premkumar,D Kanchana,Nalini Krishnan (2013). Automated Detection and Classification of Blood Diseases.
M Chayadevi,G Raju (2014). Usage of art for automatic malaria parasite identification based on fractal features.
Sneha Chavan,Ashok Narayan,Sutkar (2014). Review on agricultural plant disease detection by using image processing.
Daniel Memeu,Maitethia (2014). A rapid malaria diagnostic method based on automatic detection and classification of plasmodium parasites in stained thin blood smear images.
S Annaldas,S Shirgan,V Marathe (2014). Automatic identification of malaria parasites using image processing.
Nina Linder,Riku Turkki,Margarita Walliander,Andreas Mårtensson,Vinod Diwan,Esa Rahtu,Matti Pietikäinen,Mikael Lundin,Johan Lundin (2014). A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears.
Vishnu Makkapati,Raghuveer Rao (2009). Segmentation of malaria parasites in peripheral blood smear images.
S Raviraja,Gaurav Bajpai,Sudhir Sharma (2006). Analysis of Detecting the Malarial Parasite Infected Blood Images Using Statistical Based Approach.
C Di Rubeto,Andrew Dempster,Shahid Khan,Bill Jarra (2000). Segmentation of blood images using morphological operators.
Farnoosh Sadeghian,Zainina Seman,Abdul Ramli,Badrul Abdul Kahar,M Saripan (2009). A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing.
Sobath Premaratne,Nadira Pradeepa,Shyam Dharshani Karunaweera,W Fernando,R Supun,R Perera,Rajapaksha (2003). A neural network architecture for automated recognition of intracellular malaria parasites in stained blood films.
Chen Pan,Xiangguo Yan,Chongxun Zheng (2006). Recognition of blood and bone marrow cells using kernel-based image retrieval.
San-Kan Lee,Chien-Shun Lo,Chuin-Mu Wang,Pau-Choo Chung,Null- Chang,Ching-Wen Yang,Pi-Chang Hsu (2000). A computer-aided design mammography screening system for detection and classification of microcalcifications.
Amit Kumar,A Choudhary,P Tembhare,C Pote (2012). Enhanced identification of malarial infected objects using otsu algorithm from thin smear digital images.
Russell Howard,Masamichi Shigehikouni,Stephen Aikawa,James Aley,Andrew Leech,Thomas Lew,Joan Wellems,Diane Rener,Taylor (1986). Secretion of a malarial histidinerich protein (Pf HRP II) from Plasmodium falciparuminfected erythrocytes.
Vetle Malmberg,Marte Nilsen,Stine Langsrud,Haram (2013). Malaria in Malawi. The current status of diagnosis, prophylaxis and treatment of malaria in Malawi, how international and national guidelines can improve the situation, and how research can change these guidelines for the future.
Manjunath Salmani,B Preeti,Basavaraj Mindolli,Peerapur (2011). Comparative study of peripheral blood smear, QBC and antigen detection in malaria diagnosis.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Md. Jaffar Sadiq. 2017. \u201cReview of Microscopic Image Processing techniques towards Malaria Infected Erythrocyte Detection from Thin Blood Smears\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 17 (GJCST Volume 17 Issue F2): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJCST Volume 17 Issue F2
Pg. 7- 13

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-F Classification: J.3

Submission ReceivedDecember 13, 2016
Peer Review Double Blind
Handling Editor
Accepted January 5, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

October 21, 2017

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

Authors: Md. Jaffar Sadiq, Dr. V.V.S.S.S. Balaram (PhD/Dr. count: 1)

View Count (all-time): 264

Total Views (Real + Logic): 6770

Total Downloads (simulated): 1756

Publish Date: 2017 10, Sat

Monthly Totals (Real + Logic):

Month 1: 69 views
Month 2: 46 views
Month 3: 39 views
Month 4: 66 views
Month 5: 62 views
Month 6: 41 views
Month 7: 36 views
Month 8: 35 views
Month 9: 40 views
Month 10: 47 views
Month 11: 24 views
Month 12: 35 views
Month 13: 21 views
Month 14: 40 views
Month 15: 37 views
Month 16: 42 views
Month 17: 16 views
Month 18: 39 views
Month 19: 24 views
Month 20: 23 views
Month 21: 26 views
Month 22: 19 views
Month 23: 23 views
Month 24: 30 views
Month 25: 48 views
Month 26: 32 views
Month 27: 19 views
Month 28: 29 views
Month 29: 24 views
Month 30: 17 views
Month 31: 35 views
Month 32: 31 views
Month 33: 23 views
Month 34: 19 views
Month 35: 23 views
Month 36: 33 views
Month 37: 23 views
Month 38: 38 views
Month 39: 35 views
Month 40: 13 views
Month 41: 25 views
Month 42: 35 views
Month 43: 42 views
Month 44: 19 views
Month 45: 37 views
Month 46: 43 views
Month 47: 39 views
Month 48: 18 views
Month 49: 36 views
Month 50: 41 views
Month 51: 25 views
Month 52: 17 views
Month 53: 18 views
Month 54: 33 views
Month 55: 43 views
Month 56: 22 views
Month 57: 43 views
Month 58: 43 views
Month 59: 39 views
Month 60: 32 views
Month 61: 40 views
Month 62: 16 views
Month 63: 22 views
Month 64: 23 views
Month 65: 16 views
Month 66: 41 views
Month 67: 37 views
Month 68: 46 views
Month 69: 33 views
Month 70: 39 views
Month 71: 26 views
Month 72: 40 views
Month 73: 27 views
Month 74: 27 views
Month 75: 31 views
Month 76: 24 views
Month 77: 30 views
Month 78: 40 views
Month 79: 38 views
Month 80: 18 views
Month 81: 42 views
Month 82: 25 views
Month 83: 24 views
Month 84: 33 views
Month 85: 31 views
Month 86: 13 views
Month 87: 42 views
Month 88: 37 views
Month 89: 21 views
Month 90: 22 views
Month 91: 18 views
Month 92: 34 views
Month 93: 31 views
Month 94: 24 views
Month 95: 44 views
Month 96: 17 views
Month 97: 44 views
Month 98: 32 views
Month 99: 29 views
Month 100: 36 views
Month 101: 85 views
Month 102: 90 views

Total Views: 6770

Total Downloads: 1756

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]