Introduction: Many maternal and fetal morbidity and mortality from complications of pregnancy have been attributed to placenta abnormality. Placenta assessment in developing countries is mainly through ultrasonography which is subjective and prone to error. Objective assessment of placental abnormalities through texture analysis has been frequently done using magnetic resonance images with little done on ultrasound generated images, thus, the need for this study. The study is aimed at using statistical texture analysis in characterizing placenta tissue into normal and abnormal placenta as well as testing the accuracy of different texture analysis algorithms in differentiating placenta into normal and abnormal placental tissues.
Methods: This longitudinal study involved 500 ultrasound-generated placenta images from patients screened for adverse pregnancy outcomes in a private hospital in Enugu. These images were loaded onto an HP laptop for viewing. Two regions of interest were selected from the placenta tissue where texture features were extracted and were classified into normal and abnormal placentas using MaZda® software version 47 while the accuracy of the classification descriptors was assessed using WEKA classification algorithms.
Results: Co-occurrence matrix, run length matrix and histogram parameters differentiated normal placenta tissue from abnormal placental tissues (p-value <0.05) while variance is the only absolute gradient parameter that can differentiate normal placenta tissue from abnormal placenta tissues. All feature descriptors show high classification accuracy using KNN and ANN algorithms.
Conclusion: Texture analysis can differentiate normal placenta tissues from abnormal placenta tissue which will reduce the errors associated with subjective assessment of the placenta echogenicity.
Implications For Practice: Integrating these computer-aided algorithms into our ultrasound machines will lead to early detection of abnormal placenta tissues as early management results in better pregnancy outcomes.
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| http://dx.doi.org/10.1016/j.radi.2022.09.008 | DOI Listing |
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