Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

被引：8
|
作者：
Devi, Salam Shuleenda [1 ]
Sheikh, Shah Alam [2 ]
Laskar, Rabul Hussain [1 ]
机构：
[1] Natl Inst Technol, Elect & Commun Engn, Silchar, Assam, India
[2] Silchar Med Coll & Hosp, Silchar, Assam, India
来源：
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE | 2016年 / 4卷 / 02期
关键词：
Medical imaging; erythrocyte; malaria parasite; erythrocyte features;
D O I：
10.9781/ijimai.2016.426
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Microscopic image analysis of blood smear plays a very important role in characterization of erythrocytes in screening of malaria parasites. The characteristics feature of erythrocyte changes due to malaria parasite infection. The microscopic features of the erythrocyte include morphology, intensity and texture. In this paper, the different features used to differentiate the noninfected and malaria infected erythrocyte have been reviewed.
引用
收藏
页码：35 / 39
页数：5
相关论文
共 25 条
  • [11] Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images
    Salam Shuleenda Devi
    Rabul Hussain Laskar
    Shah Alam Sheikh
    Neural Computing and Applications, 2018, 29 : 217 - 235
  • [12] Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images
    Devi, Salam Shuleenda
    Laskar, Rabul Hussain
    Sheikh, Shah Alam
    NEURAL COMPUTING & APPLICATIONS, 2018, 29 (08) : 217 - 235
  • [13] Feature Extraction and Classification for Detection Malaria Parasites in Thin Blood Smear
    Nugroho, Hanung Adi
    Akbar, Son Ali
    Murhandarwati, E. Elsa Herdiana
    2015 2ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, COMPUTER, AND ELECTRICAL ENGINEERING (ICITACEE), 2015, : 197 - 201
  • [14] A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images
    Sengar, Neha
    Burget, Radim
    Dutta, Malay Kishore
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 224
  • [15] DLRFNet: deep learning with random forest network for classification and detection of malaria parasite in blood smear
    Murmu, Anita
    Kumar, Piyush
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (23) : 63593 - 63615
  • [16] Machine learning approach for automated screening of malaria parasite using light microscopic images
    Das, Dev Kumar
    Ghosh, Madhumala
    Pal, Mallika
    Maiti, Asok K.
    Chakraborty, Chandan
    MICRON, 2013, 45 : 97 - 106
  • [17] Malarial Parasite Detection and Recognition using Microscopic Images
    Khalid, Arslan
    Haider, Zulqarnain
    Khosa, Ikramullah
    PROCEEDINGS OF 2019 16TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGY (IBCAST), 2019, : 304 - 308
  • [18] Embedded System-Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model
    Salam, Abdus
    Hasan, S. M. Nahid
    Karim, Md. Jawadul
    Anower, Shamim
    Nahiduzzaman, Md
    Chowdhury, Muhammad E. H.
    Murugappan, M.
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2024, 34 (06)
  • [19] Malaria Detection from Blood Cell Images Using Convolutional Neural Network Model
    Tiwari, Harsha
    Dhole, Avinash
    DISTRIBUTED COMPUTING AND OPTIMIZATION TECHNIQUES, ICDCOT 2021, 2022, 903 : 603 - 612
  • [20] An automatic device for detection and classification of malaria parasite species in thick blood film
    Saowaluck Kaewkamnerd
    Chairat Uthaipibull
    Apichart Intarapanich
    Montri Pannarut
    Sastra Chaotheing
    Sissades Tongsima
    BMC Bioinformatics, 13
123