A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors Algorithm

Abstract

(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps; (ii) feature extraction, first order—standard deviation (SD), skewness (SK) and kurtosis (KR)—and second order—contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)—are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD); (iii) statistical analysis (t-test); (iv) classification with the K-Nearest Neighbors algorithm (K-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications.

High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis

“…KNN considers the similarity factor between new and available data to classify an object into predefined categories. KNN has been widely used in many fields such as industry [7][8][9], machine engineering [10], health [11][12][13], marketing [14], electrical engineering [15], security [16][17][18], manufacturing [19], energy [20][21][22], aerial [23], environment [24], geology [25,26], maritime [27,28], geographical information systems (GIS) [29], and transportation [30].…”

“…The outperformance of their method was evaluated in terms of various metrics and approved its applicability in the healthcare system for diabetes compared to other techniques. In another work [12], the researchers focused on an approach to early detect placental microcalcification by the KNN algorithm, which could lead to the improvement of maternal and fetal health monitoring during pregnancy. The results gained from real clinical datasets revealed the efficiency of the proposed model for pregnant women.…”

Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

“…Regarding the GLCM, it has a large amount of data, which is not conducive to the extraction of image features; therefore, authors in [35] defined the statistical data such as moment of contrast (Con), correlation (Cor), energy (En), and entropy (Ent) to summarize the characteristic information in GLCM. The relevant calculation methods are shown in Equations ( 1)-( 4) [36].…”