Joshua Kahn, Scott Wiese ECE533 – Fall 2003 December 12, 2003 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 Label objects 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 expert-classified reference point Average distance of 6.81 pixels Average 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