Automated Determination of a Reference Point for the ...

Automated Determination of a Reference Point for the ...

Automated Determination of a Reference Point for the Diagnosis of Spinal Instability Joshua Kahn, Scott Wiese ECE533 Fall 2003 December 12, 2003 Background Spinal Instability Caused by

degradation of spinal support tissues Painful Direct diagnosis not possible Current Diagnosis Techniques Radiograph Analysis Uncomfortable Inaccurate Compresses 3 dimensional space into 2

dimensions Clarity of radiographs is not always good Surgical Painful Complicated Proposed Technique UW Neuroradiologist has proposed a new technique based on computed tomography Rotate patient 12 degrees each way Capture CT image set of each rotation

Use digital subtraction to measure rotation Advantages Three dimensional image set More accurate than other techniques Project Goals Image subtraction software package requires a reference point for each image Our goal is to perform automated selection

of the reference point for each image Proposed Algorithm DICOM to JPEG Conversion Sobel Edge Detection Region of Interest Selection Closing (dilation followed by erosion) Black and White Edge Detection Spinal Canal Extraction Geometric Analysis 1) Convert DICOM Image to JPEG

Performed using a UNIX utility (convert) Command: convert foo.dcm jpg:foo.jpg 2) Sobel Edge Detection Detect all edges in image Performed using built-in Matlab function

3) Region of Interest Selection Minimizes area of maximum computing Does not analyze regions that are not important for algorithm 4) Closing

Minimize number of edges surrounding spinal canal Dilation followed by erosion Dilation Erosion 5) B&W Edge Detection Black and White edge detection Extracts edges of closed

image Label objects Each 8-way connected edge is a new object Each object labeled with individual marker Shown in different colors 6) Spinal Canal Extraction

Sort objects based on size Spinal canal always has the third largest area Extract each pixel from this object 7) Geometric Analysis Spinal

canal is approximated by a triangle Extract the three vertices of the triangle Estimation of the reference point is midpoint of upper two vertices Actual reference point is the intersection of the line connecting bottom vertex and estimation point with the spinal canal image 7) Geometric Analysis 8) Final Image Reference point for this image is

detected A white circle is drawn on point The coordinates of the point are output GUI Implementation Results Compared automated detection against expertclassified reference point

Average distance of 6.81 pixels Image Expected X 67 72 93 106 122 126 134 244 286 266 266 269 216

260 Expected Y 258 264 161 139 139 245 176 Average Actual X 227 282

263 267 270 223 258 Actual Y 253 270 159 143 143 249 178 X Difference Y Difference Sum of Squares

-17 -5 17.72 -4 6 7.21 -3 -2 3.61 1 4 4.12 1 4 4.12 7

4 8.06 -2 2 2.83 Average SoS: 6.81 distance well within acceptable range Conclusion Automated detection will allow more accurate diagnosis of Spinal Instability Proposed algorithm is comparable to

expert-classification of reference point Algorithm is also accurate and consistent in reference point determination

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