Model-based segmentation of medical imagery by matching distributions

• Published in: IEEE transactions on medical imaging Vol. 24; no. 3; pp. 281 - 292
• Main Authors: Freedman, D., Radke, R.J., Tao Zhang, Yongwon Jeong, Lovelock, D.M., Chen, G.T.Y.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biomedical engineering; Biomedical imaging; Cancer; Computed tomography; Computer Simulation; Deformable segmentation; Humans; Image Enhancement - methods; Image segmentation; image-guided therapy; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Male; medical image segmentation; Medical treatment; Models, Biological; Models, Statistical; Pattern Recognition, Automated - methods; Pixel; Principal Component Analysis; Probability distribution; prostate cancer; prostate segmentation; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - radiotherapy; Radiographic Image Interpretation, Computer-Assisted - methods; Radiotherapy, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; Shape; shape and appearance model; Statistical Distributions; Subtraction Technique; Systems engineering and theory; Tomography, X-Ray Computed - methods
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2004.841228

The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image. This allows for a fast, principled algorithm. We present promising results on difficult imagery for 3-D computed tomography images of the male pelvis for the purpose of image-guided radiotherapy of the prostate.