Detection of malaria parasites using digital image processing

Bashir, Ahmedelmubarak; Mustafa, Zeinab A.; Abdelhameid, Islah; Ibrahem, Rimaz

doi:10.1109/iccccee.2017.7867644

Cited by 36 publications

(14 citation statements)

References 4 publications

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“…The intensity‐based feature is extracted from the RBCs and through these features artificial neural network classifier has been trained. Accordingly, to facilitate the use of the system the graphical user interface has been developed (Bashir, Mustafa, Abdelhameid, & Ibrahem, ; Mehmood et al, ; Sharif et al, ; Yousuf et al, ).The researcher proposed a system for automatic detection of malaria parasite from the desired images. This system employs image segmentation techniques to detect malaria parasites from images acquired from Giemsa stained peripheral blood samples (Ghate, Jadhav, & Rani, ).…”

Section: Related Studiesmentioning

confidence: 99%

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Abbas

Saba

Rehman

et al. 2019

Microscopy Res & Technique

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Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K-nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature. K E Y W O R D S KNN and random forest, LBP-HSV features, Plasmodium parasite, region growing and dynamic convolution

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Section: Related Studiesmentioning

confidence: 99%

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Abbas

Saba

Rehman

et al. 2019

Microscopy Res & Technique

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show abstract

“…Correct detection rate, sensitivity and specificity were 98.66%, 98.94%, and 96.12%, respectively. Bashir et al (2017) [8] extracted set of features based on color intensity and fed them into Artificial Neural Network (ANN) for classification purpose. The accuracy of 99.68 % was achieved.…”

Section: Introductionmentioning

confidence: 99%

Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors

Ali¹,

Mohammed²

2021

IJIES

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Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.

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“…In the present time of research in the field of image processing, a noise‐free image is a noteworthy worry for separating significant data. The greater part of the researchers around the world are getting pulled in here to de‐noise an image for better visual perception [1–21]. Several state‐of‐the‐art algorithms are accounted for upgrading the image quality.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, salt‐and‐pepper noise affect the most. Salt‐and‐pepper noise comes into play while dealing with any sort of images, such as remote sensing images [4], image related to agriculture, medical image such as brain MRI (magnetic resonance imaging) images, computed tomography scan images, ultrasound images, and X‐ray images [5–7] etc. It becomes an extremely difficult task to extract the exact location and size of the brain tumour with a noisy MRI image.…”

Section: Introductionmentioning

confidence: 99%

Improved probabilistic decision‐based trimmed median filter for detection and removal of high‐density impulsive noise

Sen¹,

Rout²

2020

IET image process

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This study focuses on the detection and expulsion of noisy pixels from an image contaminated by impulsive noise. A noise detection approach is developed to avoid the misinterpretation of noise‐free pixel as noisy. In order to design the noise removal algorithm, a probabilistic decision‐based improved trimmed median filter (PDITMF) algorithm is proposed which is intended to work out the conflict related to the even number of noise‐free pixels in the trimmed median filter. It deploys two new estimation techniques for de‐noising, namely, improved trimmed median filter (ITMF) and patch else ITMF (PEITMF) as per noise density. At last, the noise detection approach is applied in the proposed PDITMF to build up a new technique called a probabilistic decision‐based adaptive improved trimmed median filter (PDAITMF) algorithm. The proposed algorithms, PDITMF and PDAITMF experiment with many standard sample images. Simulation results show the proposed algorithms are capable of detecting and de‐noising the contaminated image very efficiently and have a better visual representation. Under the authors' knowledge, the PDAITMF outperforms recently reported algorithms in context to peak signal‐to‐noise ratio as well as an image enhancement factor with the lower execution time at all noise densities.

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Detection of malaria parasites using digital image processing

Cited by 36 publications

References 4 publications

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors

Improved probabilistic decision‐based trimmed median filter for detection and removal of high‐density impulsive noise

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