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Blood Film

The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Izzet Kale – 1st expert on this subject based on the ideXlab platform

  • Adaptive Gray World-Based Color Normalization of Thin Blood Film Images
    arXiv: Computer Vision and Pattern Recognition, 2016
    Co-Authors: Andrew G. Dempster, Izzet Kale

    Abstract:

    This paper presents an effective color normalization method for thin Blood Film images of peripheral Blood specimens. Thin Blood Film images can easily be separated to foreground (cell) and background (plasma) parts. The color of the plasma region is used to estimate and reduce the differences arising from different illumination conditions. A second stage nor- malization based on the database-gray world algorithm trans- forms the color of the foreground objects to match a reference color character. The quantitative experiments demonstrate the effectiveness of the method and its advantages against two other general purpose color correction methods: simple gray world and Retinex.

  • Real time Blood image processing application for malaria diagnosis using mobile phones
    2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014
    Co-Authors: Corentin Dallet, Saumya Kareem, Izzet Kale

    Abstract:

    This paper describes a fast and reliable mobile phone Android application platform for Blood image analysis and malaria diagnosis from Giemsa stained thin Blood Film images. The application is based on novel Annular Ring Ratio Method which is already implemented, tested and validated in MATLAB. The method detects the Blood components such as the Red Blood Cells (RBCs), White Blood Cells (WBCs), and identifies the parasites in the infected RBCs. The application also recognizes the different life stages of the parasites and calculates the parasitemia which is a measure of the extent of infection.

  • ISCAS – A novel method to count the red Blood cells in thin Blood Films
    2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011
    Co-Authors: S. Kareem, R.c.s Morling, Izzet Kale

    Abstract:

    This paper describes a novel idea to identify the total number of red Blood cells (RBCs) as well as their location in a Giemsa stained thin Blood Film image. This work is being undertaken as a part of developing an automated malaria parasite detection system by scanning a photograph of thin Blood Film in order to evaluate the parasitemia of the Blood. Not only will this method eliminates the segmentation procedures that are normally used to segment the cells in the microscopic image, but also avoids any image pre-processing to deal with non uniform illumination prior to cell detection. The method utilizes basic knowledge on cell structure and brightness of the components due to Giemsa staining of the sample and detects and locates the RBCs in the image.

Stephen J Rogerson – 2nd expert on this subject based on the ideXlab platform

  • diagnosis of plasmodium falciparum malaria at delivery comparison of Blood Film preparation methods and of Blood Films with histology
    Journal of Clinical Microbiology, 2003
    Co-Authors: Stephen J Rogerson, Patrick Mkundika, Maxwell Kanjala

    Abstract:

    We compared peripheral and placental Blood Films (made by different techniques) with placental histology for diagnosis of Plasmodium falciparum malaria in pregnancy. Samples from 464 women were examined, of whom 124 (26.7%) had active P. falciparum infection and 148 (31.9%) had past infection. Placental histology was more sensitive (91%) than peripheral Blood Film (47%) or placental Blood Film (63%) examination and also detected past infection. Few women had microscopically detectable infection without a positive histology. Infection detected by histology only and past infection were both associated with significantly lower infant birth weight and with lower hemoglobin concentrations compared to the results for uninfected women. Thick Blood Films were prepared with Blood obtained by placental incision or scraping of the incision margin (263 samples) or by washing of placental tissue (235 samples). Each gave similar sensitivities (76 to 78%), specificities (98 to 99%), positive predictive values (92 to 98%), and negative predictive values (93 to 94%); but the median levels of parasitemia were lower for incision samples (840 parasites/μl) than scrapings (2,295 parasites/μl) (P = 0.02). Placental histology is the most sensitive method for the diagnosis of malaria in pregnancy. Methods for preparation of placental Films may affect the density, but not the prevalence, of P. falciparum infection detected.

Sissades Tongsima – 3rd expert on this subject based on the ideXlab platform

  • An automatic device for detection and classification of malaria parasite species in thick Blood Film
    BMC Bioinformatics, 2020
    Co-Authors: Saowaluck Kaewkamnerd, Chairat Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades Tongsima

    Abstract:

    Abstract Background Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin Blood Films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin Blood Films, which may not detect the existence of parasites due to the parasite scarcity on the thin Blood Film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick Blood Films, which contain more numbers of parasite per detection area, would address the previous limitation. Results The prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick Blood Films (40 Blood Films containing infected red Blood cells and 20 control Blood Films of normal red Blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative Blood Films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick Blood Films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%. Conclusions This work presents an automatic device for both detection and classification of malaria parasite species on thick Blood Film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

  • an automatic device for detection and classification of malaria parasite species in thick Blood Film
    BMC Bioinformatics, 2012
    Co-Authors: Saowaluck Kaewkamnerd, Chairat Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades Tongsima

    Abstract:

    Background
    Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin Blood Films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin Blood Films, which may not detect the existence of parasites due to the parasite scarcity on the thin Blood Film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick Blood Films, which contain more numbers of parasite per detection area, would address the previous limitation.

  • An automatic device for detection and classification of malaria parasite species in thick Blood Film.
    BMC bioinformatics, 2012
    Co-Authors: Saowaluck Kaewkamnerd, Chairat Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades Tongsima

    Abstract:

    Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin Blood Films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin Blood Films, which may not detect the existence of parasites due to the parasite scarcity on the thin Blood Film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick Blood Films, which contain more numbers of parasite per detection area, would address the previous limitation.
    The prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick Blood Films (40 Blood Films containing infected red Blood cells and 20 control Blood Films of normal red Blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative Blood Films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick Blood Films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%.
    This work presents an automatic device for both detection and classification of malaria parasite species on thick Blood Film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.