ReviewSensors (Basel, Switzerland)2018
Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 72 citations in OpenAlex.
- AI-Enhanced Point-of-Care Diagnostics for Infectious Diseases in Resource-Limited Settings: A Scoping Review.Tropical medicine & international health : TM & IH · 2026Article
- CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.Malaria journal · 2024Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection.Journal of imaging · 2023Article
- Tile-based microscopic image processing for malaria screening using a deep learning approach.BMC medical imaging · 2023Article
- An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis.Journal of imaging · 2022Article
- Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances.Computational intelligence and neuroscience · 2022Review
- Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models.BMC bioinformatics · 2021Article
- Segmentation of Microscope Erythrocyte Images by CNN-Enhanced Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation.Scanning · 2021Article
- Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers.Diagnostic pathology · 2020Article
- A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection.Entropy (Basel, Switzerland) · 2020Article
- Transfer Learning for Toxoplasma gondii Recognition.mSystems · 2020Article
- A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images.Data in brief · 2019Article
- Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru.Malaria journal · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images.
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