The Experts below are selected from a list of 38961 Experts worldwide ranked by ideXlab platform
Benjamin Mordmuller - One of the best experts on this subject based on the ideXlab platform.
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limit of blank and limit of detection of plasmodium falciparum thick Blood Smear microscopy in a routine setting in central africa
Malaria Journal, 2014Co-Authors: Fanny Joanny, Sascha J Z Lohr, Thomas Engleitner, Bertrand Lell, Benjamin MordmullerAbstract:Proper malaria diagnosis depends on the detection of asexual forms of Plasmodium spp. in the Blood. Thick Blood Smear microscopy is the accepted gold standard of malaria diagnosis and is widely implemented. Surprisingly, diagnostic performance of this method is not well investigated and many clinicians in African routine settings base treatment decisions independent of microscopy results. This leads to overtreatment and poor management of other febrile diseases. Implementation of quality control programmes is recommended, but requires sustained funding, external logistic support and constant training and supervision of the staff. This study describes an easily applicable method to assess the performance of thick Blood Smear microscopy by determining the limit of blank and limit of detection. These two values are representative of the diagnostic quality and allow the correct discrimination between positive and negative samples. Standard-conform methodology was applied and adapted to determine the limit of blank and the limit of detection of two thick Blood Smear microscopy methods (WHO and Lambarene method) in a research centre in Lambarene, Gabon. Duplicates of negative and low parasitaemia thick Blood Smears were read by several microscopists. The mean and standard deviation of the results were used to calculate the limit of blank and subsequently the limit of detection. The limit of blank was 0 parasites/μL for both methods. The limit of detection was 62 and 88 parasites/μL for the Lambarene and WHO method, respectively. With a simple, back-of-the-envelope calculation, the performance of two malaria microscopy methods can be measured. These results are specific for each diagnostic unit and cannot be generalized but implementation of a system to control microscopy performance can improve confidence in parasitological results and thereby strengthen malaria control.
Wenhui Wang - One of the best experts on this subject based on the ideXlab platform.
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dynamic evaluation of autofocusing for automated microscopic analysis of Blood Smear and pap Smear
Journal of Microscopy, 2007Co-Authors: Wenhui WangAbstract:Summary Autofocusing is a fundamental procedure towards automated microscopic evaluation of Blood Smear and pap Smear samples for clinical diagnosis. This paper presents comparison results of 16 selected focus algorithms based on 8000 static bright-field images and 1600 dynamic autofocusing trials using 10 Blood Smear and pap Smear samples. Besides static behaviour, dynamic autofocusing performance is introduced for ranking the 16 focus algorithms. The Fibonacci search algorithm is employed for controlling the z-motor of the microscope to reach the focus position that is determined by focus objective functions. Experimental results demonstrate that the variance algorithm provides the best overall performance. Together with our previously reported findings, it is demonstrated that the variance algorithm or the normalized variance algorithm is the optimal focus algorithm for non-fluorescence microscopy applications including pap Smear and Blood Smear imaging.
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dynamic evaluation of autofocusing for automated microscopic analysis of Blood Smear and pap Smear
Journal of Microscopy, 2007Co-Authors: Wenhui WangAbstract:Summary Autofocusing is a fundamental procedure towards automated microscopic evaluation of Blood Smear and pap Smear samples for clinical diagnosis. This paper presents comparison results of 16 selected focus algorithms based on 8000 static bright-field images and 1600 dynamic autofocusing trials using 10 Blood Smear and pap Smear samples. Besides static behaviour, dynamic autofocusing performance is introduced for ranking the 16 focus algorithms. The Fibonacci search algorithm is employed for controlling the z-motor of the microscope to reach the focus position that is determined by focus objective functions. Experimental results demonstrate that the variance algorithm provides the best overall performance. Together with our previously reported findings, it is demonstrated that the variance algorithm or the normalized variance algorithm is the optimal focus algorithm for non-fluorescence microscopy applications including pap Smear and Blood Smear imaging.
Fanny Joanny - One of the best experts on this subject based on the ideXlab platform.
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limit of blank and limit of detection of plasmodium falciparum thick Blood Smear microscopy in a routine setting in central africa
Malaria Journal, 2014Co-Authors: Fanny Joanny, Sascha J Z Lohr, Thomas Engleitner, Bertrand Lell, Benjamin MordmullerAbstract:Proper malaria diagnosis depends on the detection of asexual forms of Plasmodium spp. in the Blood. Thick Blood Smear microscopy is the accepted gold standard of malaria diagnosis and is widely implemented. Surprisingly, diagnostic performance of this method is not well investigated and many clinicians in African routine settings base treatment decisions independent of microscopy results. This leads to overtreatment and poor management of other febrile diseases. Implementation of quality control programmes is recommended, but requires sustained funding, external logistic support and constant training and supervision of the staff. This study describes an easily applicable method to assess the performance of thick Blood Smear microscopy by determining the limit of blank and limit of detection. These two values are representative of the diagnostic quality and allow the correct discrimination between positive and negative samples. Standard-conform methodology was applied and adapted to determine the limit of blank and the limit of detection of two thick Blood Smear microscopy methods (WHO and Lambarene method) in a research centre in Lambarene, Gabon. Duplicates of negative and low parasitaemia thick Blood Smears were read by several microscopists. The mean and standard deviation of the results were used to calculate the limit of blank and subsequently the limit of detection. The limit of blank was 0 parasites/μL for both methods. The limit of detection was 62 and 88 parasites/μL for the Lambarene and WHO method, respectively. With a simple, back-of-the-envelope calculation, the performance of two malaria microscopy methods can be measured. These results are specific for each diagnostic unit and cannot be generalized but implementation of a system to control microscopy performance can improve confidence in parasitological results and thereby strengthen malaria control.
Puji Budi Setia Asih - One of the best experts on this subject based on the ideXlab platform.
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morphological feature extraction from low quality of thick Blood Smear microphotographs
International Conference on Science in Information Technology, 2019Co-Authors: Umi Salamah, Anto Satriyo Nugroho, Ismail Ekoprayitno Rozi, Puji Budi Setia AsihAbstract:Identification of the parasite presence in a Blood Smear is the most crucial part of the diagnosis of malaria. In the case of the low quality of Smear, the diagnosis process becomes more complex because the parasites are difficult to read. The identification accuracy can be improved using morphological features that microscopist commonly used in diagnosing malaria. In this paper, we proposed a strategy to reduce false-positive identification of malaria parasites using rule-based on the features. This paper shows how to extract the important features and determine the rule of identification of malaria parasites. Moreover, a splitting algorithm was developed for cases where core and cytoplasm are joined in segmentation result. The proposed method was tested on 50 candidate parasite. The experimental results show that the proposed method can reduce false positives greater than 70% compared to the results of the previous system.
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a robust segmentation for malaria parasite detection of thick Blood Smear microscopic images
International Journal on Advanced Science Engineering and Information Technology, 2019Co-Authors: Umi Salamah, Anto Satriyo Nugroho, Ismail Ekoprayitno Rozi, Riyanarto Sarno, Agus Zainal Arifin, Puji Budi Setia AsihAbstract:Parasite Detection on thick Blood Smears is a critical step in Malaria diagnosis. Most of the thick Blood Smear microscopic images have the following characteristics: high noise, a similar intensity between background and foreground, and the presence of artifacts. This situation makes the detection process becomes complicated. In this paper, we proposed a robust segmentation technique for malaria parasite detection of microscopic images obtained from various endemic places in Indonesia. The proposed method includes pre-processing, Blood component segmentation using intensity slicing and morphological operation, Blood component classification utilising rule based on properties of parasite candidates, and parasite candidate formation. The performance was evaluated on 30 thick Blood Smear microscopic images. The experimental results showed that the proposed segmentation method was robust to the different condition of image and histogram. It reduced the misclassification error and relative foreground error by 2.6% and 45.5%, respectively. Properties addition to Blood component classification increased the system precision. Average of precision, recall, and F-measure of the proposed method were all 86%. It is proven that the proposed method is appropriate to be used for malaria parasites detection.
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Automated detection of Southeast Asian Ovalocytosis (SAO) obtained from thin Blood Smear microphotographs
2015 4th International Conference on Instrumentation Communications Information Technology and Biomedical Engineering (ICICI-BME), 2015Co-Authors: Eunike Sawitning Ayu Setyono, Anto Satriyo Nugroho, Fuad Ughi, Made Gunawan, Vitria Pragesjvara, Ismail Ekoprayitno Rozi, Puji Budi Setia AsihAbstract:Southeast Asian Ovalocytosis (SAO) is an erythrocyte disorder, which is characterized by oval-shaped cells with one or two transverse ridges or a longitudinal slit on Blood Smears. To check the SAO using thin Blood Smear requires expert manpower and is time consuming. This research was aimed to automatically detect the SAO from thin Blood Smear images. Digital images were acquired using a digital camera connected to a light microscope. Images underwent gray-scale conversion to decrease representation of images. Otsu?s Method was implemented to separate Blood components and the background. All clumps present in the image were then processed to be separated using recursive bottleneck detection algorithm. The major and minor axes of each extracted erythrocyte were then obtained for the calculation of ratio. The ratio determined whether the erythrocyte is normal or oval, and a count was kept between them. The percentage of oval erythrocyte over total erythrocyte was used as the determination of SAO positive or negative. The major and minor axes of the erythrocytes were successfully detected with error rate lower than 1%. Thin Blood Smear sample, prepared and provided by Eijkman Institute for Molecular Biology Indonesia, were used to build and test of the proposed algorithm.
Claudia Kuss - One of the best experts on this subject based on the ideXlab platform.
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estimating malaria parasitaemia from Blood Smear images
International Conference on Control Automation Robotics and Vision, 2006Co-Authors: Silvia Halim, Timo Bretschneider, Peter R Preiser, Claudia KussAbstract:A technique is proposed for estimating parasitaemia from Blood Smear images by extracting healthy and parasite infected red Blood cells. The developed approach accounts for uncertain imaging conditions due to microscope settings as well as the quality of the Blood Smear preparation. The solution is based on a multi-stage estimation process with minimal prior knowledge starting from a model representation of red Blood cells. Based on pattern matching with parameter optimisation and cross-validation against the expected biological characteristics, red Blood cells are determined. In a final stage, the parasitaemia measure is carried out by partitioning the uninfected and infected cells using an unsupervised and in comparison a training-based technique. Finally, the obtained estimates were analysed with respect to manually acquired results from professionals. Red Blood cells detection resulted in precision and recall rates of 80-88% and 92-98%, respectively. By using a training-based method, the precision and recall rates were improved to 92% and 95%, respectively.