Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Loddo, Andrea; Ruberto, Cecilia Di; Kocher, Michel

doi:10.3390/s18020513

Cited by 58 publications

(37 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With an appropriate threshold value RBC, the region of interest is extracted. Here foreground refers to the RBCs [3,5]. Then the binary image is super imposed with the original image so that the RGB image of extracted Red Blood Corpuscles in obtained.…”

Section: Rbc Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear

Sampathila

Shet²,

Basu³

2018

Biomed Res

View full text Add to dashboard Cite

Malaria is disease which is affecting millions of people and it is generally detected by examining the Red Blood Corpuscles (RBC) manually using microscope. However, the manual microscopic approach is time consuming, and lack of experts in the rural area, makes diagnosis of malaria very challenging one. The reported image processing approch extent the modern digital facilities to address the demand of automation, by developing a computerised facility for the detection of malaria using image processing technique. And this technological development could be a significant part of a modern digital telepathology. Proposed technology helps diagnose through the digital slide. Here the screening of microscopic images of a blood sample is achieved with color image processing approach that involves Red blood corpuscles (RBC) Segmentation, color space conversion, segmentation of the parasite, feature extraction and classification of malarial sample. The presented work detects plasmodium parasites from leishman stained microscopic blood images which in turn support pathologists for faster diagnosis. Neural network and rule based classifiers were used for the classification of blood images. The images belonging to malarial and non-malarial classes.

show abstract

Section: Rbc Segmentationmentioning

confidence: 99%

“…Even these analysis are time consuming and leads to inaccuracy and inconsistency in some cases. The computerised approach uses digitized blood slides that will be able to improve the consistence in diagnosis [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear

Sampathila

Shet²,

Basu³

2018

Biomed Res

View full text Add to dashboard Cite

show abstract

“…Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate [ 7 ], and points to other illnesses in negative cases [ 4 ]. Moreover, it is dependent on the pathologist’s skills, subjective and error-prone, [ 9 ] as it demands a high level of expertise and healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic [ 3 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Loddo et al [ 9 ] review methods that have been proposed for automatic malaria diagnosis. Each of the works reviewed is analyzed for the typical workflow in image analysis: preprocessing, segmentation, feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

Delgado-Ortet

Molina

Alférez

et al. 2020

Entropy

View full text Add to dashboard Cite

Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald–Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist’s skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks.

show abstract

“…Rajaraman et al [7] used a multi-scale Laplacian of Gaussian filter as an edge detector, and a level set activation contour technique as segmentation technique. Other works used the watershed approach to separate overlapping/touching red cells [8,9]. ROI detection is more convenient than full sliding window at different scales due to computational costs [9].…”

Section: Introductionmentioning

confidence: 99%

Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks

Ramarolahy¹,

Gyasi²,

Crimi³

2020

Preprint

View full text Add to dashboard Cite

BackgroundRecent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc) and different blood film slides preparation. Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size.MethodsTaking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) investigate transfer learning and further testing in different unseen datasets having different staining, microscope, resolution, etc. iii) a generative approach to create synthetic images which can deceive experts.ResultsThe tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear. Moreover, the generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correcly the real data for one dataset but had 50% misclassification for another dataset of images.ConclusionThe proposed deep-learning architecture performed well on a classification task for malaria parasites classification. The automated detection for malaria can help the technician to reduce their work and do not need any presence of experts. Moreover, generative networks can also be applied to blood smear images to generate useful images for microscopists. Opening new ways to data augmentation, translation and homogenization.

show abstract

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Cited by 58 publications

References 41 publications

Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear

Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks

Contact Info

Product

Resources

About