A tool for handling uncertainty in segmenting regions of interest in medical images

We have developed intelligent software tools for handling the uncertainty in delineating the boundaries of complex structures when segmenting regions of interest (ROIs) in medical images. The focus is on efficiently delineating the boundary of complex 3D organ structures, enabling accurate measurement of their structural and physiologic properties. We employ intensity based thresholding algorithms for interactive and semi-automated analysis. We also explore fuzzy-connectedness concepts in order to deal with the uncertainty in identifying organ surrounding tissue and fully automate the segmentation process. We apply the proposed tools to 3D single-photon emission computed tomography (SPECT) images visualising gastric accommodation and emptying and compare their performance to that of the manual segmentation performed by a human expert. We show that the proposed tools achieve highly accurate delineation of the complex three-dimensional gastric boundaries shown in 3D SPECT images. We also demonstrate their ability to obtain accurate volume calculations based on the segmentation procedure, in order to quantitatively assess organ functional properties such as measuring the gastric mass variation.

[1]  M W Groch,et al.  Single-photon emission computed tomography in the year 2001: instrumentation and quality control. , 2001, Journal of nuclear medicine technology.

[2]  E Bengtsson,et al.  MUSE--a new tool for interactive image analysis and segmentation based on multivariate statistics. , 1994, Computer methods and programs in biomedicine.

[3]  Azriel Rosenfeld,et al.  Fuzzy Digital Topology , 1979, Inf. Control..

[4]  Bruno M. Carvalho,et al.  Multiseeded Segmentation Using Fuzzy Connectedness , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  D S Berman,et al.  Quantification of rotational thallium-201 myocardial tomography. , 1985, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[6]  S. Webb.  The Physics of Medical Imaging , 1990 .

[7]  Jayaram K. Udupa,et al.  Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation , 2000, Comput. Vis. Image Underst..

[8]  M W Groch,et al.  Quantitative gated blood pool SPECT for the assessment of coronary artery disease at rest , 1998, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[9]  L. Clarke,et al.  MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. , 1998, Magnetic resonance imaging.

[10]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[11]  Jayaram K. Udupa,et al.  Clutter-free volume rendering for magnetic resonance angiography using fuzzy connectedness , 2000, Int. J. Imaging Syst. Technol..

[12]  Vasileios Megalooikonomou,et al.  Simultaneous assessment of gastric accommodation and emptying: studies with liquid and solid meals. , 2004, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[13]  Nikos Paragios,et al.  Gradient vector flow fast geometric active contours , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  C. Svarer,et al.  Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction. , 2004, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[15]  P. Suetens,et al.  A new thresholding method for volume determination by SPECT , 2004, European Journal of Nuclear Medicine.

[16]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[17]  J S Fleming,et al.  A rule based method for context sensitive threshold segmentation in SPECT using simulation. , 1998, Physics in medicine and biology.

[18]  P N Goodwin,et al.  SPECT instrumentation: performance, lesion detection, and recent innovations. , 1987, Seminars in nuclear medicine.

[19]  S. Resnick,et al.  An image-processing system for qualitative and quantitative volumetric analysis of brain images. , 1998, Journal of computer assisted tomography.

[20]  C. Meltzer,et al.  Brain tumor volume measurement: comparison of manual and semiautomated methods. , 1999, Radiology.

[21]  Akshay K. Singh,et al.  Deformable models in medical image analysis , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[22]  G. Herman,et al.  3D Imaging In Medicine , 1991 .

[23]  Supun Samarasekera,et al.  Multiple sclerosis lesion quantification using fuzzy-connectedness principles , 1997, IEEE Transactions on Medical Imaging.

[24]  Vasileios Megalooikonomou,et al.  Fast and effective characterization of 3D region of interest in medical image data , 2004, SPIE Medical Imaging.

[25]  M A King,et al.  Comparative evaluation of image segmentation methods for volume quantitation in SPECT. , 1992, Medical physics.

[26]  Dimitris N. Metaxas,et al.  Gibbs Prior Models, Marching Cubes, and Deformable Models: A Hybrid Framework for 3D Medical Image Segmentation , 2003, MICCAI.

[27]  Demetri Terzopoulos,et al.  Deformable models , 2000, The Visual Computer.

[28]  J S Fleming,et al.  A technique for manual definition of an irregular volume of interest in single photon emission computed tomography. , 1999, Physics in medicine and biology.

[29]  M. Camilleri,et al.  SPECT imaging of the stomach: comparison with barostat, and effects of sex, age, body mass index, and fundoplication , 2002, Gut.

[30]  Richard A. Robb,et al.  Three-dimensional visualization in medicine and biology , 2000 .

[31]  M W Groch,et al.  SPECT in the year 2000: basic principles. , 2000, Journal of nuclear medicine technology.

[32]  Ioannis A. Kakadiaris,et al.  Adaptive Fuzzy Connectedness-Based Medical Image Segmentation , 2002, ICVGIP.

[33]  B M ter Haar Romeny,et al.  Advances in three‐dimensional diagnostic radiology , 1998, Journal of anatomy.

[34]  P. Bruyant.  Analytic and iterative reconstruction algorithms in SPECT. , 2002, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.