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A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images

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@article{IJASEIT4843,
   author = {Umi Salamah and Riyanarto Sarno and Agus Zainal Arifin and Anto Satriyo Nugroho and Ismail Ekoprayitno Rozi and Puji Budi Setia Asih},
   title = {A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1450--1459},
   keywords = {Detection; intensity slicing; malaria parasites; morphological operation; thick blood smear},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4843},
   doi = {10.18517/ijaseit.9.4.4843}
}

EndNote

%A Salamah, Umi
%A Sarno, Riyanarto
%A Arifin, Agus Zainal
%A Nugroho, Anto Satriyo
%A Rozi, Ismail Ekoprayitno
%A Asih, Puji Budi Setia
%D 2019
%T A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images
%B 2019
%9 Detection; intensity slicing; malaria parasites; morphological operation; thick blood smear
%! A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images
%K Detection; intensity slicing; malaria parasites; morphological operation; thick blood smear
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4843
%R doi:10.18517/ijaseit.9.4.4843
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 4
%@ 2088-5334

IEEE

Umi Salamah,Riyanarto Sarno,Agus Zainal Arifin,Anto Satriyo Nugroho,Ismail Ekoprayitno Rozi and Puji Budi Setia Asih,"A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1450-1459, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.4843.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Salamah, Umi
AU  - Sarno, Riyanarto
AU  - Arifin, Agus Zainal
AU  - Nugroho, Anto Satriyo
AU  - Rozi, Ismail Ekoprayitno
AU  - Asih, Puji Budi Setia
PY  - 2019
TI  - A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1450
EP  - 1459
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Detection; intensity slicing; malaria parasites; morphological operation; thick blood smear
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4843
DO  - 10.18517/ijaseit.9.4.4843

RefWorks

RT Journal Article
ID 4843
A1 Salamah, Umi
A1 Sarno, Riyanarto
A1 Arifin, Agus Zainal
A1 Nugroho, Anto Satriyo
A1 Rozi, Ismail Ekoprayitno
A1 Asih, Puji Budi Setia
T1 A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1450
OP 1459
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Detection; intensity slicing; malaria parasites; morphological operation; thick blood smear
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4843
DO  - 10.18517/ijaseit.9.4.4843