The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Detty Siti Nurdiati - One of the best experts on this subject based on the ideXlab platform.
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Amniotic fluid segmentation based on pixel classification using local window information and distance angle pixel
Applied Soft Computing, 2021Co-Authors: Putu Desiana Wulaning Ayu, Sri Hartati, Aina Musdholifah, Detty Siti NurdiatiAbstract:Abstract The amniotic fluid surrounds and protects the Fetus from colliding with one another during the uterus development process. It also protects the umbilical cord from the uterine wall pressure, helps Fetus Movement, and develops muscles and bones. Selection of the most profound areas of improper and withdrawal points are not straight caliper is very likely that affect the outcome screening. Furthermore, there are similarities in texture and gray level between objects, especially in the boundary area between amniotic fluid and other objects, such as the placenta and uterus, which causes the border area to be less clear. Therefore, this research proposes a novel pixel classification model to separate amniotic fluid from other objects with a limit on the specified window size to solve this issue. In contrast to the most existing semantic segmentation methods or pixel-wise classification, we use the sampling window technique to construct train sets of data to produce pixel-level information more specifically in certain areas. Furthermore, each window extracts pixel information on gray level features and local variance (GLLV), using a novel Distance Angle Pixel (DAP). To evaluate the proposed model performance, we perform an extensive comparison with state-of-art methods by testing it on amniotic fluid ultrasound images. The results showed that the proposed model with a 3 × 3 window and random forest classifier could achieve the best value using an average Dice similarity coefficient (DSC) of 0.876, Jaccard/ IoU of 0.768, and Pixel accuracy of 85.7%. The proposed model has 0.324 DSC improved from U-Net, 0.046 from gray-level pixel classification, 0.092 from thresholding, 0,252 from active contour, and 0.19 from rectangle window sampling.
Sukri Palutri - One of the best experts on this subject based on the ideXlab platform.
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Counseling Quality of Danger Signs Pregnancy in Work Region or Rural and Urban Primary Health Center District Jeneponto
Proceedings of the International Conference on Healthcare Service Management 2018 - ICHSM '18, 2018Co-Authors: Hafidah Amiruddin, Ansariadi, Sukri PalutriAbstract:The aim of this study to determine difference of counselling quality of pregnancy dangerous signs at the public health centres (Puskesmas) of urban and rural area in Jeneponto regency.The study was used analytical observation with cross sectional study desing. The population of this study were all pregnant women in Jeneponto regency in October 2015- May 2016 at working area of urban and rural public health centre. There were 278 respondents had obtained in this study. The data analysis used computer application of SPSS examined with chi square test. The result indicated 85.3% of counselling quality of pregnancy dangerous signs in the work area of urban and rural Puskesmas was in bad category. There was difference of counselling quality of pregnancy dangerous signs such as dangerous sign component of vagina bleeding (p=0.000), severe headache (p=0.000), visual problem/blurred sight (p=0.000), swelling on face and hand (p=0.001), several abdominal pains (p=0.000), Fetus Movement was lacking or not felt (p=0.000). There was no difference of counselling quality based on age, education, employment status and parities.
Yanti, Iva Hardi - One of the best experts on this subject based on the ideXlab platform.
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Counseling Quality of Dangerous Signs of Pregnancy Health in Work Region of Urban and Rural Puskesmas (Public Health Center) Jeneponto
'ID Design 2012 DOOEL Skopje', 2020Co-Authors: Amiruddin Hafidah, Ansariadi Ansariadi, Palutturi Sukri, Wahidin, Wahidin M., Akmal, Abdul Rahman, Tasya Zhanaz, Yanti, Iva HardiAbstract:BACKGROUND: Quality healthcare is the standard of care received by citizens who are entitled to guarantee their health status due to the poor quality of health care that affect the high mortality. AIM: This study aimed to determine the difference in counseling quality of pregnancy dangerous signs at the public health centers of urban and rural areas in Jeneponto regency. METHODS: The type of study was analytical observation with a cross-sectional study design. The populations of this research are all pregnant women in Jeneponto regency in October 2015–May 2016 at the work area of Urban and Rural Public Health centers. There were 278 respondents obtained by proportionate stratified random sampling. Data analysis used computer application of SPSS examined with the Chi-square test. RESULTS: The results indicate that 85.3% of counseling quality of pregnancy dangerous signs in the work area of urban and rural Puskesmas are categorized bad. There is a difference of counseling quality of pregnancy dangerous sign component of vagina bleeding (p = 0.000), severe headache (p = 0.000), visual problems/blurred sight (p = 0.000), swelling on face and hand (p = 0.001), and severe abdominal pain (p = 0.000), Fetus Movement is lacking or not felt (p = 0.000) and fever (p = 0.000). CONCLUSION: There is no difference in counseling quality based on age, education, job, and parities
Putu Desiana Wulaning Ayu - One of the best experts on this subject based on the ideXlab platform.
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Amniotic fluid segmentation based on pixel classification using local window information and distance angle pixel
Applied Soft Computing, 2021Co-Authors: Putu Desiana Wulaning Ayu, Sri Hartati, Aina Musdholifah, Detty Siti NurdiatiAbstract:Abstract The amniotic fluid surrounds and protects the Fetus from colliding with one another during the uterus development process. It also protects the umbilical cord from the uterine wall pressure, helps Fetus Movement, and develops muscles and bones. Selection of the most profound areas of improper and withdrawal points are not straight caliper is very likely that affect the outcome screening. Furthermore, there are similarities in texture and gray level between objects, especially in the boundary area between amniotic fluid and other objects, such as the placenta and uterus, which causes the border area to be less clear. Therefore, this research proposes a novel pixel classification model to separate amniotic fluid from other objects with a limit on the specified window size to solve this issue. In contrast to the most existing semantic segmentation methods or pixel-wise classification, we use the sampling window technique to construct train sets of data to produce pixel-level information more specifically in certain areas. Furthermore, each window extracts pixel information on gray level features and local variance (GLLV), using a novel Distance Angle Pixel (DAP). To evaluate the proposed model performance, we perform an extensive comparison with state-of-art methods by testing it on amniotic fluid ultrasound images. The results showed that the proposed model with a 3 × 3 window and random forest classifier could achieve the best value using an average Dice similarity coefficient (DSC) of 0.876, Jaccard/ IoU of 0.768, and Pixel accuracy of 85.7%. The proposed model has 0.324 DSC improved from U-Net, 0.046 from gray-level pixel classification, 0.092 from thresholding, 0,252 from active contour, and 0.19 from rectangle window sampling.
Hafidah Amiruddin - One of the best experts on this subject based on the ideXlab platform.
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Counseling Quality of Danger Signs Pregnancy in Work Region or Rural and Urban Primary Health Center District Jeneponto
Proceedings of the International Conference on Healthcare Service Management 2018 - ICHSM '18, 2018Co-Authors: Hafidah Amiruddin, Ansariadi, Sukri PalutriAbstract:The aim of this study to determine difference of counselling quality of pregnancy dangerous signs at the public health centres (Puskesmas) of urban and rural area in Jeneponto regency.The study was used analytical observation with cross sectional study desing. The population of this study were all pregnant women in Jeneponto regency in October 2015- May 2016 at working area of urban and rural public health centre. There were 278 respondents had obtained in this study. The data analysis used computer application of SPSS examined with chi square test. The result indicated 85.3% of counselling quality of pregnancy dangerous signs in the work area of urban and rural Puskesmas was in bad category. There was difference of counselling quality of pregnancy dangerous signs such as dangerous sign component of vagina bleeding (p=0.000), severe headache (p=0.000), visual problem/blurred sight (p=0.000), swelling on face and hand (p=0.001), several abdominal pains (p=0.000), Fetus Movement was lacking or not felt (p=0.000). There was no difference of counselling quality based on age, education, employment status and parities.