Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels.  All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.

Inequality of health care facilities, especially radiology resources, occurs in West Java Province. There are many class A hospitals in provincial capitals, while in areas far from the provincial capital and from DKI Jakarta Province, the quantity and quality of hospitals are still lacking. Likewise with the quantity of radio diagnostic instruments and human resources. 12 radiology specialists and an additional 192 radiographers are needed in West Java Province. Archiving and image communication systems (PACS) can be used as a solution so that health workers in hospitals located far from the city or district centers can consult, and expert conclusions can be obtained from radiology specialists at referral centers. Keywords: PACS, radio diagnostic, radiology specialist, radiographer, West Java

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Purpose This study analysed the sensitivity of the field size from variations in the target volume dimensions, depth, and position. The variations in the target volume analysis were used to determine the width of the field size. Thus, the quality control of the radiation beam can be obtained. Materials and Methods The computed tomography (CT) image of the IBA Dose 1 type of water phantom consists of 350 slices. Variations in the dimension of the target volume were modelled in 10×10×10 cm3, 10×12×10 cm3 , 10.2×10×10.2 cm3, and 15×15×15 cm3. Beam parameters use one beam of irradiation on the central axis 0°, 6 MV energy, 100 cm source-skin distance (SSD), beamlet delta x, and y set to 0.1 cm. Dose distribution in the form of the XZ isodose curve and dose profile was used to observe the field size. Results In this study, the isodose curve was successfully displayed in the XZ isodose curve. The field size’s sensitivity has been successfully reviewed from variations of the target volume, depth, and position. The target X and Z direction analysis is used in determining the width and length of the field size. Conclusion The analysis related to the field size sensitivity study was obtained from a relatively valid calculation. The field size was evaluated with variations in depth of 1.5 cm, 5 cm, 10 cm, and variations in positions of 10 cm, 12 cm, 14 cm, 18 cm, and 20 cm. This study will be used as a reference to validate the distribution of computational environment for radiotherapy research (CERR) dose in the future. Thus, the accuracy of the dose calculation can be obtained.