Automatic detection of graticule isocenter and scale from kV and MV images

To automate the detection of isocenter and scale of the mechanical graticule on kilo-voltage (kV) or mega-voltage (MV) films or electronic portal imaging device (EPID) images.

We developed a robust image processing approach to automatically detect isocenter and scale of mechanical graticule from digitized kV or MV films and EPID images. After a series of preprocessing steps applied to the digital images, a combination of Hough transform and Radon transform was performed to detect the graticule axes and isocenter. The magnification of the graticule was automatically detected by solving an optimization problem using golden section search and parabolic interpolation algorithm. Tick marks of the graticule were then determined by extending from isocenter along the graticule axes with multiples of the magnification value. This approach was validated using 23 kV films, 26 MV films, and 91 EPID images in different anatomical sites (head-and-neck, thorax, and pelvis). Accuracy was measured by comparing computer detected results with manually selected results.

The proposed approach was robust for kV and MV films of varying image quality. The isocenter was detected within 1 mm for 98% of the images. The exceptions were three kV films where the graticule was not actually visible. Of all images with correct isocenter detection, 99% had a magnification detection error less than 1% and tick mark detection error less than 1 mm, with the exception of 1 kV film (magnification error: 3.17%; tick mark error: 1.29 mm) and 1 MV film (magnification error: 0.45%; tick mark error: 1.11 mm).

We developed an approach to robustly and automatically detect graticule isocenter and scale from two-dimensionla (2D) kV and MV films. This is a first step toward automated treatment planning based on 2D x-ray images.