We investigated comparisons between patient dose and noise in pelvic, abdominal, thoracic and head CT images using an automatic method. 113 patient images (37 pelvis, 34 abdominal, 25 thoracic, and 17 head examinations) were retrospectively and automatically examined in this study. Water-equivalent diameter (Dw), size-specific dose estimates (SSDE) and noise were automatically calculated from the center slice for every patient image. The Dw was calculated based on auto-contouring of the patients’ edges, and the SSDE was calculated as the product of the volume CT dose index (CTDIvol) extracted from the Digital Imaging and Communications in Medicine (DICOM) header and the size conversion factor based on the Dw obtained from AAPM 204. The noise was automatically measured as a minimum standard deviation in the map of standard deviations. A square region of interest of about 1 cm2 was used in the automated noise measurement. The SSDE values for the pelvis, abdomen, thorax, and head were 21.8 ± 7.3 mGy, 22.0 ± 4.5 mGy, 21.5 ± 4.7 mGy, and 65.1 ± 1.7 mGy, respectively. The SSDEs for the pelvis, abdomen, and thorax increased linearly with increasing Dw, and for the head with constant tube current, the SSDE decreased with increasing Dw. The noise in the pelvis, abdomen, thorax, and head were 5.9 ± 1.5 HU, 5.2 ± 1.4 HU, 4.9 ± 0.8 HU and 3.9 ± 0.2 HU, respectively. The noise levels for the pelvis, abdomen, and thorax of the patients were relatively constant with Dw because of tube current modulation. The noise in the head image was also relatively constant because Dw variations in the head are very small. The automated approach provides a convenient and objective tool for dose optimizations.

Texture is one of the most important features for image analysis, which provides informations such as the composition of texture on the surface structure, changes of the intensity, or brightness. Gray level co-occurence matrix (GLCM) is a method that can be used for statistical texture analysis. GLCM has proven to be the most powerful texture descriptors used in image analysis. This study uses the four-way GLCM 0o, 45o, 90o, and 135o. Support vector machine (SVM) is a machine learning that can be used for image classification. SVM has a high generalization capability without any requirement of additional knowledge, even with the high dimension of the input space. The data used in this study are the image of goat meat, buffalo meat, horse meat, and beef with shooting distance 20 cm, 30 cm and 40 cm. The result of this study shows that the best recognition rate of 87.5% was taken at a distance of 20 cm with neighboring pixels distance d = 2 in the direction GLCM 135o.

The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from −1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (NG). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (NM) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the NM value was much smaller than the NG. For kernel sizes from 17 × 17 to 21 × 21 pixels, the NM value was about 90% of NG. And for kernel sizes of 23 × 23 pixels and above, NM is greater than NG. It was also found that even with small kernel sizes the relationship between NM and NG is linear with R2 more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.

Work plan and budget are solutions in gathering budget planning data and uniforming budgeting. the purpose of the work plan and budget is to assist all work units in preparing and managing the budget plan without mixing it with other units. The importance of performances analysis method and PIECES framework were chosen in this study because both of method were able to classify the insitution problems, opportunities and information system goals. PIECES Framework as a questionnaire criteria to responding the users and the Importance Performance Analysis method as a result of questionnaire data from 88 respondents in the form of Quadrants. The aim of the research to evaluate information systems is a user satisfaction and importance of information systems. The calculation results with IPA method shows that the user's satisfaction averages and the importance of information system quality is 93.71%. However, there are a few deficiencies that need to be improved in the development of information systems, work plans and annual budgets such as system quality, accurate informations, estimated cost of building the system, security systems efficiency and service improvement for users.

We proposed and evaluated a water-equivalent diameter calculation without using a region of interest (ROI), (Dw,t) and compared it with the results of using a ROI fitted to the patient border (Dw,f). Evaluations were carried out on thoracic and head CT images. We found that the difference between Dw,t and Dw,f was within 5% for all images in the head region, and most images were within 5% (27 of the 30 patients, 90%) in the thoracic region. We also proposed a method to automatically detect and eliminate the patient table (or head support) from images and evaluated the water-equivalent diameter values after the table had been removed (Dw,nt). This method was able to recognize and remove the patient table from all images used. By removing the table, the water-equivalent diameter (Dw,nt) became more accurate and the difference from Dw,f was within 5% for all images (head and thoracic images).