Feature extraction and classification for detection malaria parasites in thin blood smear

Nugroho, Hanung Adi; Akbar, Son Ali; Murhandarwati, E. Elsa Herdiana

doi:10.1109/icitacee.2015.7437798

Cited by 43 publications

(28 citation statements)

References 6 publications

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“…In another work by Nugroho et al, 2015 [ 12 ], segmentation of MPs was made through a k-means algorithm. Using histogram-based texture features, the authors aimed to detect the parasites in three different life cycle stages using a multilayer perceptron with backpropagation algorithm, with reported SE of 81.7% and SP of 90.8%.…”

Section: Related Workmentioning

confidence: 99%

Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination

Rosado

Costa

Elias

et al. 2017

Sensors

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Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times.

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Section: Related Workmentioning

confidence: 99%

Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination

Rosado

Costa

Elias

et al. 2017

Sensors

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“…Ruberto et al proposed the combination of automatic thresholding and morphological approach to detect and classify malaria parasites [5]. Furthermore, Akbar et al [6] introduced combination of k-means clustering and morphological operation methods on HSV colour model to segment P. falciparum on the thin blood films. Then, several shape and texture features were extracted and classified by using MLP classifier to classify P. falciparum stage into three classes, i.e.…”

Section: Figure 1 the Life Cycle Of Malaria [2]mentioning

confidence: 99%

Feature analysis for stage identification of Plasmodium vivax based on digital microscopic image

Nugroho

Maysanjaya

Murhandarwati

et al. 2019

IJEECS

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Plasmodium parasite is identified to confirm malaria disease. Paramedics need to observe the presence of this parasite prepared on thick and thin blood films under microscope. However, false identification still occurs which is caused by human factor during the examination. Thus, malaria identification based on digital image processing has been widely developed to overcome the error possibility. This paper proposes a scheme to identify and classify the stages of Plasmodium vivax parasite on digital microscopic image of thin blood films based on feature analysis. Shape and texture features are extracted from segmented parasite objects. Feature selection based on wrapper method is then conducted to obtain relevant features which may contribute in improving the classification result. The classification process is conducted based on Naïve Bayes classifier. The performance of proposed method is evaluated using 73 digital microscopic images of P.vivax parasite on thin blood films comprising of 29 trophozoites, 10 schizonts and 34 gametocytes stages. By using six selected features including perimeter, dispersion, mean of intensity, ASM, contrast GLCM and entropy GLCM, the proposed scheme achieves the best classification rate with the accuracy, sensitivity and specificity of 97.29%, 97.30% and 97.30%, respectively. This indicates that the proposed scheme has a potential to be implemented in the development of a computerised aided malaria diagnosis system for assisting the paramedics.

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“…Tek et al [14] [15], used image subtraction from a known image whereas Das et al [6] adapted the 'Gray World assumption' for illumination correction. To achieve noise elimination, Median filtering has been adapted by most authors like, Ruberto et al [16], Ross et al [17], Anggraini et al [18], Das et al [6], Sutkar et al [19], Chandra et al [20], Rosado et al [21], Predanan et al [22], Bahendwar et al [23] and Nugroho et al [24]. Authors Dave et al [25] and Savkare et al [26] have used a combination of Median filtering with Laplacian filter for noise removal along with enhancement of the edge region.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Elter et al [47], used green and blue channels for obtaining threshold value, followed by morphological Top-Hat to determine parasite region. The authors Nugroho et al [24] used K-NN classifier with 'S component' in HSV colour space for segmentation. Mushabe et al [40] used K-NN classifier to identify the parasite and non-parasite regions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Use of Artificial Neural Networks (ANN) for segmentation and parasite classification is used by Chandra et al [20], Bahendwar et al [23] (with RGB and HIS features) and Nugroho et al [24] (with statistical and textural features). Authors Diaz et al [31], used different histogram features with Support Vector Machine (SVM) and multilayer perceptron for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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Feature extraction and classification for detection malaria parasites in thin blood smear

Cited by 43 publications

References 6 publications

Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination

Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination

Feature analysis for stage identification of Plasmodium vivax based on digital microscopic image

Malaria Parasitaemia Estimation � A new perspective (Preprint)

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