During the last quarter of the twentieth century, there has been a significant expansion in the means by which parents in the United States might use technologies to watch their children. Watching and worrying about children are not new to the job of parenthood, but the ways of watching now available to parents represents a change of degree so great as to represent a change in kind. The parental gaze has become technologized. This dissertation investigates what happens when man-made devices insert themselves into this most basic of human endeavors. Parenting desires, social expectations, and technological capacities have co-evolved in the United States to a point where the norms of parental watching are increasingly technology-based. This is a "mixed methods," cross-case study. It delves into the particulars of three distinct media while looking for patterns of use and effects across the different technologies. The core of this investigation is three case studies of particular surveillance technologies that all came to prominence, in terms of their popularity or frequency of use, in the United States in the last thirty years. The three subjects of these case studies--fetal ultrasound, Eisenberg, Murkoff, and Hathaway's 1984 pregnancy advice and guide book What to Expect When You're Expecting, and baby monitors--are all media that offer parents the opportunity to be better and less anxious parents by enhancing their powers of parenting observation. They form an optical--textual--acoustic triad that demonstrates the breadth of media that are enlisted into surveillance practices. These new anxiety technologies change thinking, perceptions, and attitudes. They serve both to introduce new human capacities and to direct and to mold existing capacities. They have also helped to change our ideas of what is possible. A few overarching characteristics of American parental thinking have helped to pushed surveillance to prominence. Middle class American parents of the last quarter of the twentieth century have come to feel that the world is a more dangerous place for their children. They perceive their offspring as more vulnerable to dangers and as less capable of avoiding these dangers on their own. Parents also feel an increased sense of personal responsibility for the safety of their children. It is not that that contemporary parents have warmer or deeper feelings toward their children, but rather that contemporary parents believe that they both can and should control a much broader range of dangers to their children than parents in the past believed they could control. The "anxiety technologies" of this study serve in part to bring home to their users the riskiness of parenting and the vulnerability of the fetus/infant. These technologies have come to promote responsibility expansion, efficiency orientation, and risk focus for parents. While these technologies do provide parents with a great deal more focused information, many of the perceived enhancements in powers to effect outcomes are presumptive, illusory effects of actual increases in information. Information without influence is as likely to contribute to anxiety as to power.

Ultrasound-based fetal biometry is used to derive important clinical information for identifying IUGR (intra-uterine growth restriction) and managing risk in pregnancy. Accurate and reproducible biometric measurement relies heavily on a good standard image plane. However, qualitative visual assessment, which includes the visual identification of certain anatomical landmarks in the image is prone to inter- and intra-reviewer variability and is also time-consuming to perform. Automated anatomical structure detection is the first step towards the development of a fast and reproducible quality assessment of fetal biometry images. This thesis deals specifically with abdominal scans in the development and evaluation of methods to automatically detect the stomach and the umbilical vein within them. First, an original method for detecting the stomach and the umbilical vein in fetal abdominal scans was developed using a machine learning framework. A classifier solution was designed with AdaBoost learning algorithm with Haar features extracted from the intensity image. The performance of the new method was compared on different clinically relevant gestational age groups. Speckle and the low contrast nature of ultrasound images motivated the idea of introducing features extracted from local phase images. Local phase is contrast invariant and has proven to be useful in other ultrasound image analysis application compared with intensity. Nevertheless, it has never been implemented in a machine learning environment before. In our second experiment, local phase features were proven to have higher discriminative power than intensity features which enabled them to be selected as the first weak classifiers with large classifier weight. Third, a novel approach to improving the speed of the detection was developed using a global feature symmetry map based on local phase to select the candidate locations for the stomach and the umbilical vein. It was coupled with a local intensity-based classifier to form a “hybrid” detector. A nine-fold increase in the average computational speed was recorded along with higher accuracy in the detection of both the anatomical structures. Quantitative and qualitative evaluations of all the algorithms were presented using 2384 fetal abdominal images retrieved from the image database study of the Oxford Ultrasound Quality Control Unit of the INTERGROWTH-21st project. Finally, the “hybrid” detection method was evaluated in two potential application scenarios. The first application was clinical scoring in which both the computer algorithm and four experts were asked to record presence or absence of the stomach and the umbilical vein in 400 ultrasound images. The computer-experts agreement was found to be comparable with the inter-expert agreement. The second application concerned selecting the standard image plane from 3D abdominal ultrasound volume. The algorithm was successful in selecting 93.36% of the images plane defined by the expert in 30 ultrasound volumes.

Vitamin D is very important for endochondral ossification and it is commonly insufficient during pregnancy (Javaid et al., 2006). Insufficiency of vitamin D during pregnancy predicts bone mass and hence predicts adult osteoporosis (Javaid et al., 2006). The relationship between maternal vitamin D and manually measured fetal biometry has been studied (Mahon et al., 2009). However, manual fetal biometry especially volumetric measurements are subjective, time-consuming and possibly irreproducible. Computerised measurements can overcome or at least reduce such problems. This thesis concerns the development and evaluation of novel methods to do this. This thesis makes three contributions. Firstly, we have developed a novel technique based on the Random Forests (RF) classifier to segment and measure several fetal femoral characteristics from 3D ultrasound volumes automatically. We propose a feature selection step in the training stage to eliminate irrelevant features and utilise the "good" ones. We also develop a weighted voting mechanism to weight tree probabilistic decisions in the RF classifier. We show that the new RF classifier is more accurate than the classic method (Yaqub et al., 2010b, Yaqub et al., 2011b). We achieved 83% segmentation precision using the proposed technique compared to manually segmented volumes. The proposed segmentation technique was also validated on segmenting adult brain structures in MR images and it showed excellent accuracy. The second contribution is a wavelet-based image fusion technique to enhance the quality of the fetal femur and to compensate for missing information in one volume due to signal attenuation and acoustic shadowing. We show that using image fusion to increase the image quality of ultrasound images of bony structures leads to a more accurate and reproducible assessment and measurement qualitatively and quantitatively (Yaqub et al., 2010a, Yaqub et al., 2011a). The third contribution concerns the analysis of data from a cohort study of 450 fetal femoral ultrasound volumes (18-21 week gestation). The femur length, cross-sectional areas, volume, splaying indices and angles were automatically measured using the RF method. The relationship between these measurements and the fetal gestational age and maternal vitamin D was investigated. Segmentation of a fetal femur is fast (2.3s/volume), thanks to the parallel implementation. The femur volume, length, splaying index were found to significantly correlate with fetal gestational age. Furthermore, significant correlations between the automatic measurements and 10 nmol increment in maternal 25OHD during second trimester were found.