The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Izzet Kale - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Gray World-Based Color Normalization of Thin Blood Film Images
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Andrew G. Dempster, Izzet KaleAbstract: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.
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Real time Blood image processing application for malaria diagnosis using mobile phones
2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014Co-Authors: Corentin Dallet, Saumya Kareem, Izzet KaleAbstract: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.
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ISCAS - A novel method to count the red Blood cells in thin Blood Films
2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011Co-Authors: S. Kareem, R.c.s Morling, Izzet KaleAbstract: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.
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Computer vision for microscopy diagnosis of malaria
Malaria Journal, 2009Co-Authors: F. Boray Tek, Andrew G. Ag Dempster, Izzet KaleAbstract:This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin Blood Film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.
Stephen J Rogerson - One of the best experts on this subject based on the ideXlab platform.
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diagnosis of plasmodium falciparum malaria at delivery comparison of Blood Film preparation methods and of Blood Films with histology
Journal of Clinical Microbiology, 2003Co-Authors: Stephen J Rogerson, Patrick Mkundika, Maxwell KanjalaAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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An automatic device for detection and classification of malaria parasite species in thick Blood Film
BMC Bioinformatics, 2020Co-Authors: Saowaluck Kaewkamnerd, Chairat Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades TongsimaAbstract: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.
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an automatic device for detection and classification of malaria parasite species in thick Blood Film
BMC Bioinformatics, 2012Co-Authors: Saowaluck Kaewkamnerd, Chairat Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades TongsimaAbstract: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.
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An automatic device for detection and classification of malaria parasite species in thick Blood Film.
BMC bioinformatics, 2012Co-Authors: Saowaluck Kaewkamnerd, Chairat Uthaipibull, Apichart Intarapanich, Montri Pannarut, Sastra Chaotheing, Sissades TongsimaAbstract: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.
I Kale - One of the best experts on this subject based on the ideXlab platform.
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A novel method to count the red Blood cells in thin Blood Films
2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011Co-Authors: S. Kareem, R.c.s Morling, I KaleAbstract: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.
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parasite detection and identification for automated thin Blood Film malaria diagnosis
Computer Vision and Image Understanding, 2010Co-Authors: Andrew G. Dempster, I KaleAbstract:This paper investigates automated detection and identification of malaria parasites in images of Giemsa-stained thin Blood Film specimens. The Giemsa stain highlights not only the malaria parasites but also the white Blood cells, platelets, and artefacts. We propose a complete framework to extract these stained structures, determine whether they are parasites, and identify the infecting species and life-cycle stages. We investigate species and life-cycle-stage identification as multi-class classification problems in which we compare three different classification schemes and empirically show that the detection, species, and life-cycle-stage tasks can be performed in a joint classification as well as an extension to binary detection. The proposed binary parasite detector can operate at 0.1% parasitemia without any false detections and with less than 10 false detections at levels as low as 0.01%.
John Ngoyi - One of the best experts on this subject based on the ideXlab platform.
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external quality assessment of giemsa stained Blood Film microscopy for the diagnosis of malaria and sleeping sickness in the democratic republic of the congo
Bulletin of The World Health Organization, 2013Co-Authors: Pierre Mukadi, Philippe Gillet, Albert Lukuka, Benjamin Atua, Nicole Sheshe, Albert Kanza, Jean Bosco Mayunda, Briston Mongita, Raphael Senga, John NgoyiAbstract:OBJECTIVE: To report the findings of a second external quality assessment of Giemsa-stained Blood Film microscopy in the Democratic Republic of the Congo, performed one year after the first. METHODS: A panel of four slides was delivered to diagnostic laboratories in all provinces of the country. The slides contained: (i) Plasmodium falciparum gametocytes; (ii) P. falciparum trophozoites (reference density: 113 530 per µl); (iii) Trypanosoma brucei subspecies; and (iv) no parasites. FINDINGS: Of 356 laboratories contacted, 277 (77.8%) responded. Overall, 35.0% of the laboratories reported all four slides correctly but 14.1% reported correct results for 1 or 0 slides. Major errors included not diagnosing trypanosomiasis (50.4%), not recognizing P. falciparum gametocytes (17.5%) and diagnosing malaria from the slide with no parasites (19.0%). The frequency of serious errors in assessing parasite density and in reporting false-positive results was lower than in the previous external quality assessment: 17.2% and 52.3%, respectively, (P < 0.001) for parasite density and 19.0% and 33.3%, respectively, (P < 0.001) for false-positive results. Laboratories that participated in the previous quality assessment performed better than first-time participants and laboratories in provinces with a high number of sleeping sickness cases recognized trypanosomes more frequently (57.0% versus 31.2%, P < 0.001). Malaria rapid diagnostic tests were used by 44.3% of laboratories, almost double the proportion observed in the previous quality assessment. CONCLUSION: The overall quality of Blood Film microscopy was poor but was improved by participation in external quality assessments. The failure to recognize trypanosomes in a country where sleeping sickness is endemic is a concern.
