Automatic estimation of orientation and position of spine in digitized X-rays using mathematical morphology.
In this paper, we propose a method for automatic determination of position and orientation of spine in digitized spine X-rays using mathematical morphology. As the X-ray images are usually highly smeared, vertebrae segmentation is a complex process. The image is first coarsely segmented to obtain the location and orientation information of the spine. The state-of-the-art technique is based on the deformation model of a template, and as the vertebrae shape usually shows variation from case to case, accurate representation using a template is a difficult process. The proposed method makes use of the vertebrae morphometry and gray-scale profile of the spine. The top-hat transformation-based method is proposed to enhance the ridge points in the posterior boundary of the spine. For cases containing external objects such as ornaments, H-Maxima transform is used for segmentation and removal of these objects. The Radon transform is then used to estimate the location and orientation of the line joining the ridge point clusters appearing on the boundary of the vertebra body. The method was validated for 100 cervical spine X-ray images, and in all cases, the error in orientation was within the accepted tolerable limit of 15 degrees. The average error was found to be 4.6 degrees. A point on the posterior boundary was located with an accuracy of +/-5.2 mm. The accurate information about location and orientation of the spine is necessary for fine-grained segmentation of the vertebrae using techniques such as active shape modeling. Accurate vertebrae segmentation is needed in successful feature extraction for applications such as content-based image retrieval of biomedical images.
Kumar
V P Dinesh
VP
Department of Electronics, Cochin University of Science and Technology, Cochin, Kerala 682022, India.
Thomas
Tessamma
T
D000465:Algorithms; D001185:Artificial Intelligence; D002574:Cervical Vertebrae / Q000000981:diagnostic imaging; D001330:Electronic Data Processing; D006801:Humans; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted; D011859:Radiography; D012815:Signal Processing, Computer-Assisted; D013131:Spine / Q000000981:diagnostic imaging*