MalariaCount: An image analysis-based program for the accurate determination of parasitemia

Sio, Selena W. S.; Sun, Wei; Kumar, Sanjeev; Bin, Wong Zeng; Tan, Soon Shan; Ong, Sim Heng; Kikuchi, Haruhisa; Oshima, Yoshiteru; Tan, Kevin S. W.

doi:10.1016/j.mimet.2006.05.017

Cited by 116 publications

(91 citation statements)

References 18 publications

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“…[17] suggested clump splitting algorithm and rule base approach to segment out clump erythrocytes from peripheral blood smear images. [18] applied rule based approach for P. falciparum infection detection purpose. Most of the literatures showed malaria classification based on cultured blood smear sample.…”

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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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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“…for quantitative evaluation of the level of parasites in the blood, has been described in [1]. The presented system is based on the detection of edges representing cell and parasite boundaries.…”

Section: Releted Workmentioning

confidence: 99%

RBCs and Parasites Segmentation from Thin Smear Blood Cell Images

Panchbhai¹,

Damahe²,

Nagpure³

et al. 2012

IJIGSP

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Abstract-Manually examine the blood smear for the detection of malaria parasite consumes lot of time for trend pathologists. As the computational power increases, the role of automatic visual inspection becomes more important. An automated system is therefore needed to complete as much work as possible for the identification of malaria parasites. The given scheme based on used of RGB color space, G layer processing, and segmentation of Red Blood Cells (RBC) as well as cell parasites by auto-thresholding with offset value and use of morphological processing. The work compare with the manual results obtained from the pathology lab, based on total RBC count and cells parasite count. The designed system successfully detects malaria parasites and RBC cells in thin smear image.

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“…Image analysis based automated counting approaches can be found in many fields, such as medical imaging, horticulture & crop industry and soil research [1], [2] and [3]. They show the efficacy of automated counting systems, which are sufficiently reliable, consistent, fast and also more convenient than manual counting.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the challenges for counting are uneven illumination, noise, occluded objects and clumped objects. For example, Sio, S. W. S. et al developed a clump splitting technique to address the issue of counting clumped red blood cells [3]. The clumped cells adversely affect the accuracy of the parasitimia, because one clumped cell contains a few cells, but it is counted only once.…”

Section: Introductionmentioning

confidence: 99%