Steroid and eicosanoid metabolism occurs in two phases and primarily within hepatic tissues, but localized metabolism has been examined in several extra-hepatic tissues in humans and rodents. Phase I of metabolism is performed by Cytochrome P450s (CYP) that add hydroxyl groups to the carbon ring structure which is further metabolized by phase II UDP-glucuronosyltransferase (UGT). The overall objectives of the following experiments were to: 1) determine the amount of extra-hepatic steroid metabolism within reproductive tissues of cattle across the estrous cycle; 2) determine the amount of extra-hepatic steroid metabolism and an oxylipin profile within reproductive tissues of cattle based on pregnancy status; and 3) determine the amount of endometrial blood perfusion in cattle using a novel laser Doppler technique. Activity of CYP1A was found within corpora lutea (CL) tissues of both pregnant and non-pregnant cattle, but not within endometrial tissues. Endometrial perfusion, measured using a novel laser Doppler technique, was also validated by measuring angiogenic factors in close proximity to the location of perfusion. A positive correlation (r = 0.28; P = 0.04) was observed between endometrial perfusion and nitrite concentration, an angiogenic factor. Endometrial blood perfusion was affected by the proximity to the CL, but not by the proximity of the dominant follicle. In addition, UGT was categorized across the estrous cycle and the activity was dependent upon the proximity of the CL. Oxylipins, including eicosanoids, were also profiled in CL of cattle that were non-pregnant and pregnant with 5 out of 39 oxylipins differentially expressed. The activity and oxylipin products of steroid and eicosanoid enzymes were not correlated with serum or luteal progesterone. Through these experiments, we have verified that there is localized metabolism of steroids and eicosanoids within reproductive tissues of cattle as well as fetal tissues. Also, we have achieved a full oxylipin profile of non-pregnant and pregnant cattle CL with five oxylipins contained in various amounts between pregnancy status.

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.