A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and...

Area of Science:

• Medical Imaging
• Obstetrics
• Biomedical Engineering

Background:

• Ultrasonography is standard for assessing fetal well-being and placental health during pregnancy.
• Placental microcalcifications are common aging signs, but early detection may indicate dysfunction.
• Current methods may lack sensitivity for subtle microcalcifications.

Purpose of the Study:

• To develop an advanced ultrasonography technique for improved placental microcalcification detection.
• To enhance the analysis of placental structure using statistical features and fractal dimension.
• To improve the early identification of placental dysfunction.

Main Methods:

• Image preprocessing and region of interest cropping.
• Feature extraction including first and second-order statistics (SD, SK, KR, C, H, CR, E, EN) from Gray Level Co-occurrence Matrix (GLCM) and fractal dimension (FD).
• Classification using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms.

Main Results:

• The proposed method demonstrated enhanced detectability and visibility of placental microcalcifications.
• Experimental results on clinical data confirmed the effectiveness of the new approach.
• Improved image analysis led to better identification of microcalcifications.

Conclusions:

• The novel ultrasonography approach significantly improves placental microcalcification detection.
• This technique holds potential for earlier and more accurate diagnosis of placental dysfunction.
• Further research can refine this method for routine obstetric practice.