Iranian Journal of Biomedical Engineering (IJBME)
نوع مقاله : مقاله کامل پژوهشی
نویسندگان
1 کارشناس ارشد مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان
2 دانشیار گروه مهندسی پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی اصفهان
3 دانشیار، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی اصفهان
4 استادیار گروه ارتودنسی، دانشکده دندان پزشکی، دانشگاه علوم پزشکی اصفهان
کلیدواژهها
موضوعات
عنوان مقاله English
نویسندگان English
Cephalometry is the scientific measurement of head dimensions to predict craniofacial growth, plan treatment and compare different cases. There have been many attempts to automate cephalometric analysis with the aim of reducing the time required to obtain an analysis, improve the accuracy of landmark identification and reduce the errors due to clinician subjectivity. This paper introduces a method for automatic landmark detection on cephalograms. We introduced a combination of model-based methods and neural networks on cephalograms. For this purpose, first some feature points were extracted using a nonlinear diffusion filter and Susan Edge Detector to model the size, rotation, and translation of skull. A neural network was used to classify the images according to their geometrical specifications. Using learning vector quantization (L VQ) for every new image, the possible coordinates of landmarks were estimated. Then a modified active shape model (ASM) was applied and a local search to find the best match to the intensity profile was used and every point was moved to get the best location. Finally, a sub-image matching procedure was applied to pinpoint the exact location of each landmark. In order to evaluate the results of this method, 20 randomly selected images were used with a drop-one-out method. Each image had a dimension of about 170x200 mm, digitized in 100 dpi (4 pixel == 1mm). On average, 24% of the 16% landmarks were within 1mm of correct coordinates, 61 percent within 2 mm, and 93 percent within 5 mm. the proposed method in this study has had a distinct improvement over the other proposed methods of automatic landmark detection.
کلیدواژهها English
