Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT) |
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| Authors: | Wei Lu Jinglu Tan |
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| Affiliation: | 1. Robarts Research Institute, University of Western Ontario, London, ON, Canada;2. Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada;3. Medical Biophysics, University of Western Ontario, London, ON, Canada;4. Neurosurgery, Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada;5. Department of Electronic Engineering, City University of Hong Kong, PR China;1. Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;2. Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea;3. Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 6B3, Canada |
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| Abstract: | An iterative randomized Hough transform (IRHT) is developed for detection of incomplete ellipses in images with strong noise. The IRHT iteratively applies the randomized Hough transform (RHT) to a region of interest in the image space. The region of interest is determined from the latest estimation of ellipse parameters. The IRHT “zooms in” on the target curve by iterative parameter adjustments and reciprocating use of the image and parameter spaces. During the iteration process, noise pixels are gradually excluded from the region of interest, and the estimation becomes progressively close to the target. The IRHT retains the advantages of RHT of high parameter resolution, computational simplicity and small storage while overcoming the noise susceptibility of RHT. Indivisible, multiple instances of ellipse can be sequentially detected. The IRHT was first tested for ellipse detection with synthesized images. It was then applied to fetal head detection in medical ultrasound images. The results demonstrate that the IRHT is a robust and efficient ellipse detection method for real-world applications. |
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