Abstract
B-mode ultrasound imaging segmentation is facing a challenge in the artifacts such as speckle noise, blurry edges, low contrast, and unexpected shadow. This study proposed a model segmentation considering the local information from each pixel based upon its neighborhood information. The features used are a statistical texture (mean intensity, deviation standard, skewness, entropy, and property) taken based upon the 3 × 3 and 5 × 5 window. Random forest was used to classify each pixel into three regions: the amniotic fluid, uterus, and fetal body. An evaluation was carried out by calculating the comparison between the ground truth area and the segmentation results of the proposed model. The experimental results showed that the proposed model has an average accuracy of 81.45% in the 3 × 3 window and 85.86% in the 5 × 5 window on 50 tested images.
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Acknowledgment
The author would like thank to the Research Directorate of Gadjah Mada University for funding this research in the RTA (Rekognisi Tugas Akhir) 2019 scheme. The author also would thank the Surya Husada Hospital, Bali, for supporting this research in providing data.
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Ayu, D.W., Hartati, S., Musdholifah, A. (2019). Amniotic Fluid Segmentation by Pixel Classification in B-Mode Ultrasound Image for Computer Assisted Diagnosis. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_5
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DOI: https://doi.org/10.1007/978-981-15-0399-3_5
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