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The challenge of detecting and tracking moving objects in imaging throughout the atmosphere stems from the atmospheric turbulence effects that cause time-varying image shifts and blur. These phenomena significantly increase the miss and false detection rates in long-range horizontal imaging. An efficient method was developed, which is based on novel criteria for objects' spatio-temporal properties, to discriminate true from false detections, following an adaptive thresholding procedure for foreground detection and an activity-based false alarm likeliness masking. The method is demonstrated on significantly distorted videos and compared with state of the art methods, and shows better false alarm and miss detection rates.

Objective-To evaluate the feasibility of machine learning (ML) tools for segmenting and classifying first-trimester fetal brain ultrasound images.Methods-Two image segmentation methods processed high-resolution fetal brain images obtained during the nuchal translucency scan: "Statistical Region Merging" (SRM) and "Trainable Weka Segmentation" (TWS), with training and testing sets in the latter. Measurement of the fetal cerebral cortex in original and processed images served to evaluate the performance of the algorithms. Mean absolute percentage error (MAPE) was used as an accuracy index of the segmentation processing.Results-The SRM plugin revealed a total MAPE of 1.71% AE 1.62 SD (standard deviation) and a MAPE of 1.4% AE 1.32 SD and 2.72% AE 2.21 SD for the normal and increased NT groups, respectively. The TWS plugin displayed a MAPE of 1.71% AE 0.59 SD (testing set). There were no significant differences between the training and testing sets after 5-fold cross-validation. The images obtained from normal NT fetuses and increased NT fetuses revealed a MAPE of 1.52% AE 1.02 SD and 2.63% AE 1.98 SD.Conclusions-Our study demonstrates the feasibility of using ML algorithms to classify first-trimester fetal brain ultrasound images and lay the foundation for earlier diagnosis of fetal brain abnormalities.

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