Real time blood image processing application for malaria diagnosis using mobile phones

Dallet, Corentin; Kareem, S.; Kale, I.

doi:10.1109/iscas.2014.6865657

Cited by 25 publications

(14 citation statements)

References 5 publications

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“…For this purpose two structuring element of size 25% of radius of RBC and 75% of radius of RBC in pixels are considered. Dilation of the grey scale image I at any location (x, y) by structuring element (se) is calculated to be the maximum value of the neighbourhood outlined by se whose origin is at (x, y) is given [17] by equation (2.4).…”

Section: Morphological Dilationmentioning

confidence: 99%

“…The structuring element of disc format and size equal to 75% of radius of RBC in terms of pixels is used for erosion. The grey scale image I with a structuring element se for location (x, y) is given [17] …”

Section: Morphological Erosionmentioning

confidence: 99%

“…The first is the annular ring whose outer radius is equal to 75% of radius of RBC in pixels and the inner radius is equal to 25% of radius of RBC in pixels. Let I o be the average intensity with in the outer annular ring region and I i be the average intensity with in the inner region [17]. The ARR is given by the equation (2.6).…”

Section: Annular Ring Ratio (Arr)mentioning

confidence: 99%

“…The scheme in [4-6] uses colour and morphology based algorithm to identify the infected cells. The scheme in [16,17] uses Annular Ring Ratio (ARR) method to segment the RBCs and histogram based thresholding to identify the infected cells in the blood smear image. In [29,30] the detection of presence of parasite in blood smear is based on histogram based thresholding.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of Malarial and Babesiosis Parasite in RBC using Combination of Annular Ring Ratio and Marker Controlled Watershed Segmentation

Preeth¹,

Abisha²

2017

DJ JAECE

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Malaria is a disease caused by a single celled protozoan parasite. It can be fatal if not treated at early stage. The main goal of this paper is to provide an efficient algorithm to detect the presence of malarial parasite even at early stages. This method makes use of a combination algorithm of Annular Ring Ratio (ARR) and marker controlled watershed algorithm to identify the Red Blood Cells (RBC) in the blood smear image. The platelets and artifacts are removed by using morphological operations before identifying the RBC. The presence of malarial parasite is detected by using thresholding based on colour as the Giemsa stained nucleus are always blue. Using the same method the presence of Babesiosis parasite in blood can also be identified.

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Section: Morphological Dilationmentioning

confidence: 99%

Section: Morphological Erosionmentioning

confidence: 99%

Section: Annular Ring Ratio (Arr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Malarial and Babesiosis Parasite in RBC using Combination of Annular Ring Ratio and Marker Controlled Watershed Segmentation

Preeth¹,

Abisha²

2017

DJ JAECE

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“…,Elter et al (2011),Anggraini et al (2011a),Berge et al (2011), Sio et al (2007, DiRuberto et al (2001Ruberto et al ( , 2002,Proudfoot et al (2008), Fang et al (2011), Ross et al (2006), Prasad et al (2012), Kumarasamy et al (2011), Diaz et al (2009), Somasekar et al (2013), Ghate & Jadhav (2012), Ahirwar et al (2012), Kareem et al (2012a, b), Vink et al (2013), Le et al (2008), Chayadevi & Raju(2014),May et al (2013),Mehrjou et al (2013),Gatc et al (2013),Malihi et al (2013),Premaratne et al (2003),Frean (2009), Halim et al (2006,Suhanic et al (2009), Savkare & Narote (2011), Anand et al (2008,Anggraini et al (2011b),Cesario et al (2012),Dallet et al (2014),Gering & Atkinson (2004),Suryawanshi & Dixit (2013), Linder et al (2014,Ahmed (2012) …”

mentioning

confidence: 99%

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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Smartphone-Supported Malaria Diagnosis Based on Deep Learning

Yang

Silamut

et al. 2019

Machine Learning in Medical Imaging

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Real time blood image processing application for malaria diagnosis using mobile phones

Cited by 25 publications

References 5 publications

Detection of Malarial and Babesiosis Parasite in RBC using Combination of Annular Ring Ratio and Marker Controlled Watershed Segmentation

Detection of Malarial and Babesiosis Parasite in RBC using Combination of Annular Ring Ratio and Marker Controlled Watershed Segmentation

Computational microscopic imaging for malaria parasite detection: a systematic review

Smartphone-Supported Malaria Diagnosis Based on Deep Learning

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