Calculation of sharpness in lung images of pleural effusion patients and normal lung images using the thresholding segmentation method

Research on the calculation of tapering from lung's images of patients with pleural effusion and normal lungs has been carrying out using the thresholding segmentation method. The tapering calculation was done using the Matlab programming language by applying the thresholding segmentation method's image processing theory. Images sharpness was obtaining from calculating the longest distance from all distances that were searching in the program. The steps taken in this research were image quality improvement, determination of the region of interest (ROI), thresholding segmentation, and calculating the tilt. Taper count was performing on eight lung images identified pleural effusion and eight lung images identified as normal. In 8 images of lungs pleural effusions, each taper was obtained 166; 159; 167; 167; 150; 157; 114; and 149. Whereas in 8 images of normal lungs, it was obtained that the respective curls were 187; 174; 181; 198; 199; 195; 179; and 195. The analysis showed that the lung's images of pleural effusion patients had a tapering of less than 171. In contrast, normal lung images had a tapering of more than 171, so that one characteristic was obtained that could distinguish between normal lungs and pleural effusions. It can facilitate medical personnel in the early detection of pleural effusion patients so that they can be handled quickly and accurately.