Malaria parasite detection in giemsa-stained blood cell images

Abstract

this research represents a method to detect malaria parasite in blood samples stained with giemsa. In order to increase the accuracy of detecting, at the first step, the red blood cell mask is extracted. It is due to the fact that most of malaria parasites exist in red blood cells. Then, stained elements of blood such as red blood cells, parasites and white blood cells are extracted. At the next step, red blood cell mask is located on the extracted stained elements to separate the possible parasites. Finally, color histogram, granulometry, gradient and flat texture features are extracted and used as classifier inputs. Here, five classifiers were used: support vector machines (SVM), nearest mean (NM), K nearest neighbors (KNN), 1-NN, and Fisher. In this research K nearest neighbors classifier had the best accuracy, which was 91%.

Computational microscopic imaging for malaria parasite detection: a systematic review

“…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,…”

“…Features Type Khan et al (2011),Špringl (2009), Ahirwar et al (2012, Vink et al (2013), Annaldas et al (2014), Chavan & Sutkar (2014), Savkare & Narote (2011), Nithyaa et al (2013), Wahyuningrum & Indrawan (2012), Das et al (2012Das et al ( , 2013Das et al ( , 2015 Histogram-based (mean, standard deviation skewness, kurtosis, energy, entropy) Tek et al (2009, 2010), Di Ruberto et al (2002, Malihi et al (2013), Cesario et al (2012) Colour histogram Das et al (2013Das et al ( , 2015 Entropy Ross et al (2006), Das et al (2011Das et al ( , 2012Das et al ( , 2013Das et al ( , 2015, Yunda et al (2011), Maity et al (2012),Špringl (2009), Vink et al (2013) Haralick texture Ross et al (2006), Maity et al (2012),Špringl, (2009), Das et al (2012Das et al ( , 2013Das et al ( , 2014Das et al ( , 2015 Grey-level run length matrices (GLRLM) Das et al (2012Das et al ( , 2013Das et al ( , 2015, Maity et al (2012), Linder et al (2014 Local binary pattern (LBP) Das et al (2012Das et al ( , 2013Das et al ( , 2015,…”

Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

“…Malaria Parasite Detection in Giemsa-Stained Blood Cell Images based on features i.e. gradient, flat texture, colour histogram, area granulometry had been studied [22]. Malaria diagnostic system using morphological features has also been proposed [23].…”

Development of inexpensive blood imaging systems: where are we now?

“…Algorithmic, computer-based detection and diagnosis of malaria is the subject of continuing research [41][42][43][44][45], and could aid in the implementation of these new technologies by lowering barriers to adoption and increasing access to care. However, an intriguing concept has been recently developed that combines the power of digital imaging with human ingenuity.…”