Digital pelvic radiographs are used to identify the locations of implanted iodine-125 seeds and their numbers after insertion. However, it is difficult and laborious to visually identify and count all implanted seeds on the pelvic radiographs within a short time. Therefore, our purpose in this research was to develop an automated method for estimation of the number of implanted seeds based on two-view analysis of pelvic radiographs. First, the images of the seed candidates on the pelvic image were enhanced using a difference of Gaussian filter, and were identified by binarizing the enhanced image with a threshold value determined by multiple-gray level thresholding. Second, a simple rule-base method using ten image features was applied for false positive removal. Third, the candidates for the likely number of a multiply overlapping seed region, which may include one or more seeds, were estimated by a seed area histogram analysis and calculation of the probability of the likely number of overlapping seeds. As a result, the proposed method detected 99.9% of implanted seeds with 0.71 false positives per image on average in a test for training cases, and 99.2% with 0.32 false positives in a validation test. Moreover, the number of implanted seeds was estimated correctly at an overall recognition rate of 100% in the validation test using the proposed method. Therefore, the verification time for the number of implanted seeds could be reduced by the provision of several candidates for the likely number of seeds.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Health, Toxicology and Mutagenesis