The detection of ischemia heart disease was usually scored by a trained nuclear medicine Physician by determining the ischemia location and size subjectively (by eyes). This subjective method will add to the 5% tolerance error, which might compromise the whole process of treatment especially in patients with severe conditions. The aim of this study is to increase the edge recognition in cardiac scintography images in patients with ischemic heart disease using L*a*b* color space and K-means clustering. First, we read the nuclear cardiac images. We then to convert the images form RGB color space to L*a*b* color space. Then we classify the colors in 'a*b*' space using K-means clustering. Then we label every pixel in the Image using the results from K-means. We then create images that segment the cardiac image by colour. Finally, we segment the cardiac image into a separate image. The sample of this study was (146 cases) and they showed increase enhancement. This segmentation technique (automatic scoring) and segmented images was adjudicated by three nuclear medicine physician as being comparable to other segmentation techniques created with manual editing (subjective scoring). This technique showed potentials increasing of detection of the myocardial ischemia rather than conventional one.

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Ectopic Pregnancy (EP) is a serious emergency faced by physicians in obstetrics and gynaecology, and the identification of EP can be frequently missed. The common signs of ectopic pregnancy in fertile women are lower abdominal discomfort and vaginal hemorrhage. The aim of this paper was to study EP patients to determine the etiology and management of EP. This study aims to study the hazardous factors, types, and clinical complications associated with EP patients, as well as diagnostic methods and prognosis. This was a retrospective cohort study performed at King Khalid Hospital, Majmaah, Saudi Arabia. Patient demographic data were documented. These data included parity, hazardous factors, signs and symptoms, habits, occupation, past history of PID, ectopic pregnancy, pelvic surgery and management. The data were analyzed using statistical software in MS Windows. The data are presented as the mean plus standard deviation. In our study, most cases (83.3%) were managed surgically, which means that too few cases were given medical or conservative options; thus, medical staff training in how to apply medical or conservative management techniques according to guideline criteria can improve future outcomes.

Ultrasound consider of one of the most important tool in analysis of fetus development. In ultrasound images, the recognition closely adjacent tissues is very crucial process because of the noise that affected both image quality and sharpness. This study conducted to study the fetus images improvement using HAT-TOP transform as computing choice in order to increase the diagnostic accuracy in diagnosis of neonatal diseases. Many image-processing techniques were used to improve the images including Using HAT-TOP and Blind Deconvolution Algorithm. The results of the study showed HAT-TOP was best processing filter and define the fetus precisely.