Segmentation based approach for detection of malaria parasites using moving k-means clustering

Nasir, Aimi Salihah Abdul; Mashor, Mohd Yusoff; Mohamed, Zeehaida

doi:10.1109/iecbes.2012.6498073

Cited by 49 publications

(21 citation statements)

References 8 publications

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“…Mushabe et al [40] used K-NN classifier to identify the parasite and non-parasite regions. Nasir et al [48], used the saturation channel of image in HSI colour space and moving Knearest neighbour (MKNN) to differentiate parasite containing red blood cell. Khan et al [49] performed clustering on the 'b' component of the image converted to Lab colour space for obtaining parasite region.…”

Section: Literature Reviewmentioning

confidence: 99%

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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“…Parasite type Diaz et al (2009), Anggraini et al (2011a, Hanif et al (2011), Purwar et al (2011, Sio et al (2007), Elter et al (2011), Mavandadi et al (2012), Kareem et al (2012aKareem et al ( , 2012b, Vink et al (2013), Premaratne et al, (2003), Lee et al (2011), Luengo-Oroz et al (2012, Anggraini et al (2011b), Linder et al (2014) P. falciparum Ghosh et al (2011a) Das et al (2011), Abdul-Nasir et al (2012, von Mühlen (2004) P. vivax Das et al (2012Das et al ( , 2013, Prasad et al (2012), Makkapati & Rao, (2011), Ross et al (2006, Kaewkamnerd et al (2012), Yunda et al (2011), Maity et al (2012, Khan et al (2014) P. falciparum and P. vivax Suwalka et al (2012), Khan et al (2011), Soni (2011, Soni et al (2011), Komagal et al (2013), Ahirwar et al (2012), Tek et al (2006Tek et al ( , 2009Tek et al ( , 2010, Malihi et al (2013), Gitonga et al (2014), Thung & Suwardi (2011) P. falciparum, P. vivax, P. malariae and P. ovale Ma et al (2010), Proudfoot et al (2008) P. yoelii,…”

Section: Authorsmentioning

confidence: 99%

“…Kareem et al (2012aKareem et al ( , 2012b introduced angular ring ratio method towards identification of P. falciparum gametocyte. Moving K-means clustering has also been used for extracting parasite infected regions (Abdul Nasir et al, 2012). To identify chromatin dot, optimal threshold on saturation channel has been detected (Makkapati & Rao, 2009).…”

Section: Authorsmentioning

confidence: 99%

“…Effective for overlapping cell segmentation and may fail to segment highly overlapped cell segmentation Tek et al (2006), von Mühlen ( Splitting clumped algorithm It can produce blocky segment Walliander et al (2013) Combination of adaptive histogram thresholds and morphology Useful where distinctive picks of a histogram are not present Khan et al (2014), Abdul-Nasir et al(2012 K-means clustering It has no role towards overlapping cell segmentation and difficult to select initial centroid Savkare & Narote (2011), Wahyuningrum & Indrawan (2012) Watershed algorithm Watershed segmentation algorithm provides continuous boundary region but it has tendency towards over segmentation Suryawanshi & Dixit (2013) Poisson distribution thresholding Not effective for overlapping cell segmentation (9) Gradient texture: It measures the velocity changes of grey intensity value of a target erythrocyte. Edge information is calculated from gradient images (Špringl, 2009).…”

Section: Microscopic Feature Extractionmentioning

confidence: 99%

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Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

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Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.

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“…However, the manual recognition method is time consuming and effortful especially in situation where large number of samples require reliable analysis. Therefore, fast and efficient methods are required for detection of malaria parasites in order to avoid the untrue diagnosis of malaria [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of different types of Malaria Diseases using First Order Features

Ajala¹,

Funmilola²,

Fenwa³

et al. 2015

IJAIS

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Malaria is majorly caused by three parasitic organisms namely, P. malaria, P. vivax, P. falciparum. Physician (Microbiologists, Laboratory technicians, Medical Practitioners, and medical experts) examines erythrocytes under light microscope to study the color and morphological changes toward malaria diagnosis. Assessment accuracy depends on the physician-pathological understanding. To help physicians in cases where they may be wrong, developing a computer assisted malaria parasite detection tool has helped modern pathological services where a physician is able to get assistance in order to quickly make better decision toward malaria diagnosis. Segmentation of medical images helps in identifying features that are needed for clinical diagnosis. First order features always give accurate information about the medical image and useful for identification and analysis. In this work, first order features which entails the mean, median, mode, standard deviation, energy, Skewness, kurtosis, area, entropy were extracted from the infected malaria images. The corresponding results show a significant steady value which could be useful during classification of medical images. The work is ongoing, the aim of the work is to classify the malaria parasite and to come up with a diagnostic system that will be able to diagnose tissue and blood cell based diseases like malaria, Ebola etc.

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Segmentation based approach for detection of malaria parasites using moving k-means clustering

Cited by 49 publications

References 8 publications

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Computational microscopic imaging for malaria parasite detection: a systematic review

Comparative Analysis of different types of Malaria Diseases using First Order Features

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