KIDNEY STONE DETECTION FROM DICOM IMAGES USING IMAGE PROCESSING TECHNIQUES

Kidney stone detection from medical images is a critical step in early diagnosis and treatment planning. In this study, an image processing approach is proposed to identify kidney stones from DICOM (Digital Imaging and Communications in Medicine) files, focusing on both right and left kidney regions. The methodology includes grayscale conversion, elliptical area cropping, thresholding, and pixel counting to calculate kidney stone size. The results indicate significant differences in processing time and threshold values depending on the image characteristics. Based on the analysis, the shortest processing time was 1.30 seconds, while the longest reached 1.46 seconds, with an average of 1.32 seconds. It was also observed that images without kidney stones required approximately 1 second of processing time, while those containing kidney stones took between 20 to 40 seconds, depending on the size and number of stones. In one case (Sample 14), the processing time reached 2.21 seconds, reflecting the high pixel count (542 pixels) and larger stone area (322.59 mm²). The correlation analysis showed that the right kidney had a moderate positive relationship (R = 0.57, R² = 0.33), while the left kidney had a stronger correlation (R = 0.94, R² = 0.88), indicating more consistent pixel-to-area relationships. These findings demonstrate the effectiveness of the proposed image processing approach in identifying kidney stones with high accuracy, providing valuable insights for clinical decision-making and future improvements in automated kidney stone detection systems.