Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. It can timely find the fetal abnormality so that necessary action can be taken by the pregnant woman. Firstly, a detailed literature has been offered on fetal biometric parameters and nuchal translucency to highlight the investigation approaches with a degree of validation in diverse clinical domains. Then, a categorization of the bibliographic assessment of recent research effort in the segmentation field of ultrasound 2D fetal images has been presented. The fetal images of high-risk pregnant women have been taken into the routine and continuous monitoring of fetal parameters. These parameters are used for detection of fetal weight, fetal growth, gestational age, and any possible abnormality detection.

This is a preliminary study and the objective of this study has been to compare the performance of some of the primitive and fundamentally different post acquisition image enhancement algorithms as applied to ultrasound (US) liver images. Such a comparison would help to decide as to which algorithm could be useful for clinicians, and in evaluating the role of US liver image enhancement in a soft-copy environment. In this study, 10 US liver images were taken, and 5 fundamentally different and widely employed image enhancement techniques were applied on these images. As the principal objective of image enhancement is to obtain an image with a high content of visual detail, a multipoint rank-order method was used to identify small differences or trends in observations. Among the different algorithms, the morphological filtering outperformed other techniques. The images with diffused liver diseases had a preference of 76% and the images with cystic masses had a preference of 67%. Wavelet-based filtering closely followed this with 63% of preference in respect of images with a diffused liver disease and 57% of preference in respect of images with cystic masses.

Currently, radiologists indicate the femur endpoints with an interactive marker device; however, these measurements are subjective and have proved to be inconsistent. The main objective of this work is to obtain a time-efficient morphology-based algorithm to recognize femur contour in fetal ultrasound images, refine its shape for automatic length measurement, and thus, attaining accuracy and reproducibility of measurement. To achieve these objectives a cross-sectional study with subjects belonging to different family units of different communities was carried out. The images obtained from the subjects were initially processed using morphological operators to remove the background from the image. Thereafter, to refine the shape of the femur, the images were metamorphosed, using the morphological operators, till a single pixel - wide skeleton of the femur was available in the most time-effective manner. The skeleton-end-points are assumed to be the femur-end-points, and the femur length is calculated as the distance between the end-points to estimate gestational age. The mean execution time of the proposed algorithm was around 4 seconds. Measurements, performed using the automation algorithm, were found to be closely correlated to those obtained manually. The proposed algorithm was found to be time-efficient, and the results obtained were comparable to those derived through the existing methods for estimation of gestational age.

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