Segmenting Ultrasound B-Mode Images Using RiIG Distributions and Stochastic Optimization
In this paper, we propose a novel algorithm for
delineating the endocardial wall from a human heart ultrasound scan.
We assume that the gray levels in the ultrasound images are
independent and identically distributed random variables with
different Rician Inverse Gaussian (RiIG) distributions. Both synthetic
and real clinical data will be used for testing the algorithm. Algorithm
performance will be evaluated using the expert radiologist evaluation
of a soft copy of an ultrasound scan during the scanning process and
secondly, doctor’s conclusion after going through a printed copy of
the same scan. Successful implementation of this algorithm should
make it possible to differentiate normal from abnormal soft tissue and
help disease identification, what stage the disease is in and how best
to treat the patient. We hope that an automated system that uses this
algorithm will be useful in public hospitals especially in Third World
countries where problems such as shortage of skilled radiologists and
shortage of ultrasound machines are common. These public hospitals
are usually the first and last stop for most patients in these countries.
<p>[1] T. McInerney and D. Terzopoulos, “Deformable models in medical
image analysis in mathematical methods,” pp 171– 180. IEEE,1996.
[2] [H. C. Inyiama Engr. V. C. Chijindu and Engr. G.Uzedhe, “Medcal
imagesegmentation methodologies: A classified overview,”African
Journal of Computing and ICT, 2006.
[3] T.F. Cootes, A. Hill, C.J. Taylor, and J. Haslam.“Use of active shape
models for locating structures in medical images,”Image and vision
computing.vol. 12, no. 6, pp..355–365, 1994.
[4] R.F. Wagner, S.W. Smith, J.M. Sandrik, and H. Lopez,. “Statistics of
speckle in ultrasound b-scans,”IEEE Trans. SonisUltrasonics.,vol. 30,
no. 3, pp. 156–163, 1983.
[5] P. Atkinson and MV Berry,. “Random noise in ultrasonic echoes
diffracted by blood,”Journal of Physics A: Mathematical, Nuclear and
General,vol.7, no. 11, pp. 1293, 2001.
[6] C.B. Burckhardt, “Speckle in ultrasound b-modescans. Sonics and
Ultrasonics,”IEEETransactions, vol. 25, no. 1, pp.1–6, 1978.
[7] S.W. Flax, G.H. Glover, and N.J. Pelc, “Textural variations in b-mode
ultrasonography: a stochastic model,” Ultrasonic Imaging,vol. 3, no. 3,
pp.235–257, 1981.
[8] G. Kossoff, WJ Garrett, DA Carpenter, J. Jellins,and MJ Dadd,,
“Principles and classification of soft tissues by grey scale echography,”
Ultrasound in Medicine & Biology, vol. 2, no. 2, pp. 89–105, 1976.
[9] F.G Sommer, L.F Joynt, BA Carroll, and A. Macovski, “Ultrasonic
characterization of abdominal tissues via digital analysis of
backscattered waveforms,”Radiology,vol. 141, no. 3, pp. 811–817, 1981.
[10] L.F. Joynt, “A stochastic approach to ultrasonic tissue
characterization,”PhD thesis, Stanford University, Carlifornia, USA,
1979.
[11] T. Eltoft. “The rician inverse Gaussian distribution: a new model for
non-rayleigh signal amplitude statistics.”Image Processing,IEEE
Transactions on, vol..14, no. 11, pp. 1722– 1735, 2005.
[12] O.Francois. “Global optimization with exploration/selection algorithms
and simulated annealing,”The Annals of Applied Probability, vol. 12,no.
1, pp. 248–271, 2002.
[13] E. Brusseau, C.L. de Korte, F. Mastik, J. Schaar, and A.F.W. van
derSteen, “Fully automated luminal contour segmentation in
intracoronary ultrasound imaging-a statistical approach,” Medical
Imaging, IEEE Transactionson,vol. 23, no. 5, pp. 554–566, 2004.
[14] M. Mignotte, J. Meunier, and J.C. Tardif, “Endocardial boundary
estimation and tracking in echocardiographic images using deformable
template and markov random fields,”Pattern Analysis &
Applications,vol. 4, no. 4, pp. 256–271, 2001.
[15] J. Richy, “Compressive Sensing in Medical Ultrasonography”. PhD
thesis, KTH, 2012.
[16] M. Kohli, Youtube.(2012, June 20), “Ultrasound Physics 2 –
Interactions with tissue”, [Video File],
http://www.youtube.com/watch?v=Q1YPZ-04dag
[17] T.F. Cootes and C.J. Taylor, “Active shape models - smart snakes,” in
Proc. British Machine Vision Conference,vol. 266275, Citeseer, 1992.
[18] T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham, et al, “Active shape
models-their training and application,” Computer vision and image
understanding,vol. 61, no. 1, pp. 38– 59, 1995.
[19] T. Cootes.,“An introduction to active shape models,” Image Processing
and Analysis, pp. 223–248, 2000.
[20] M.B. Stegmann and D.D. Gomez, “A brief introduction to statistical
shape analysis,”Informatics and Mathematical Modelling, Technical
University of Denmark, DTU, pp.15, 2002.
[21] K.W. Wan, K.M. Lam, and K.C. Ng, “An accurate active shape model
for facial feature extraction,”Pattern Recognition Letters, vol. 26, no. 15,
pp. 2409–2423, 2005.
[22] F. Destrempes, J. Meunier, M.F. Giroux, G. Soulez, and G. Cloutier,
“Segmentation in ultrasonic¡ emphasis,” MedicalImaging, IEEE
Transactions on, vol. 28, no. 2, pp. 215–229, 2009
[23] B.S. Garra D.G. Brown M.F. Insana, R.F. Wagner and T.H. Shawker,
“Analysis of ultrasound image texture via generalized
ricianstatistics,”Optical Engineering, vol. 25, no. 6, pp. 743–748,1986.
[24] P.M.Shankar. “A compound scattering pdf for the ultrasonic echo
envelope and its relationship to k and nakagami
distribution,”IEEETransactions,Ferroelect., Freq. Contol
[25] P.M. Shankar,“A general statistical model for ultrasonic scattering from
tissues,”IEEE Trans. Ultrasonic.Ferroelect., Freq. Contol
[26] Y.Meiry, YouTube, (2012, Jan 13), “Lecture 13 (Basics of MRI,
Ultrasound,”, [Video File]
http://www.youtube.com/watch?v=w3Ybm4A2GhI
[27] D.Kroon, University of Twente, Febuary2010., [Code]
[28] R. Werner, Allianz, Group Risk Controlling, (2006, May 1) [Code],
Germany
[29] J. L. Romeu, “The Chi-Square: a large Sample Goodness of Fit Test,”
Reliability Analysis Center, vol. 10, no. 4, pp. 1-6, START 2003-2004.</p>
<p>[1] T. McInerney and D. Terzopoulos, “Deformable models in medical
image analysis in mathematical methods,” pp 171– 180. IEEE,1996.
[2] [H. C. Inyiama Engr. V. C. Chijindu and Engr. G.Uzedhe, “Medcal
imagesegmentation methodologies: A classified overview,”African
Journal of Computing and ICT, 2006.
[3] T.F. Cootes, A. Hill, C.J. Taylor, and J. Haslam.“Use of active shape
models for locating structures in medical images,”Image and vision
computing.vol. 12, no. 6, pp..355–365, 1994.
[4] R.F. Wagner, S.W. Smith, J.M. Sandrik, and H. Lopez,. “Statistics of
speckle in ultrasound b-scans,”IEEE Trans. SonisUltrasonics.,vol. 30,
no. 3, pp. 156–163, 1983.
[5] P. Atkinson and MV Berry,. “Random noise in ultrasonic echoes
diffracted by blood,”Journal of Physics A: Mathematical, Nuclear and
General,vol.7, no. 11, pp. 1293, 2001.
[6] C.B. Burckhardt, “Speckle in ultrasound b-modescans. Sonics and
Ultrasonics,”IEEETransactions, vol. 25, no. 1, pp.1–6, 1978.
[7] S.W. Flax, G.H. Glover, and N.J. Pelc, “Textural variations in b-mode
ultrasonography: a stochastic model,” Ultrasonic Imaging,vol. 3, no. 3,
pp.235–257, 1981.
[8] G. Kossoff, WJ Garrett, DA Carpenter, J. Jellins,and MJ Dadd,,
“Principles and classification of soft tissues by grey scale echography,”
Ultrasound in Medicine & Biology, vol. 2, no. 2, pp. 89–105, 1976.
[9] F.G Sommer, L.F Joynt, BA Carroll, and A. Macovski, “Ultrasonic
characterization of abdominal tissues via digital analysis of
backscattered waveforms,”Radiology,vol. 141, no. 3, pp. 811–817, 1981.
[10] L.F. Joynt, “A stochastic approach to ultrasonic tissue
characterization,”PhD thesis, Stanford University, Carlifornia, USA,
1979.
[11] T. Eltoft. “The rician inverse Gaussian distribution: a new model for
non-rayleigh signal amplitude statistics.”Image Processing,IEEE
Transactions on, vol..14, no. 11, pp. 1722– 1735, 2005.
[12] O.Francois. “Global optimization with exploration/selection algorithms
and simulated annealing,”The Annals of Applied Probability, vol. 12,no.
1, pp. 248–271, 2002.
[13] E. Brusseau, C.L. de Korte, F. Mastik, J. Schaar, and A.F.W. van
derSteen, “Fully automated luminal contour segmentation in
intracoronary ultrasound imaging-a statistical approach,” Medical
Imaging, IEEE Transactionson,vol. 23, no. 5, pp. 554–566, 2004.
[14] M. Mignotte, J. Meunier, and J.C. Tardif, “Endocardial boundary
estimation and tracking in echocardiographic images using deformable
template and markov random fields,”Pattern Analysis &
Applications,vol. 4, no. 4, pp. 256–271, 2001.
[15] J. Richy, “Compressive Sensing in Medical Ultrasonography”. PhD
thesis, KTH, 2012.
[16] M. Kohli, Youtube.(2012, June 20), “Ultrasound Physics 2 –
Interactions with tissue”, [Video File],
http://www.youtube.com/watch?v=Q1YPZ-04dag
[17] T.F. Cootes and C.J. Taylor, “Active shape models - smart snakes,” in
Proc. British Machine Vision Conference,vol. 266275, Citeseer, 1992.
[18] T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham, et al, “Active shape
models-their training and application,” Computer vision and image
understanding,vol. 61, no. 1, pp. 38– 59, 1995.
[19] T. Cootes.,“An introduction to active shape models,” Image Processing
and Analysis, pp. 223–248, 2000.
[20] M.B. Stegmann and D.D. Gomez, “A brief introduction to statistical
shape analysis,”Informatics and Mathematical Modelling, Technical
University of Denmark, DTU, pp.15, 2002.
[21] K.W. Wan, K.M. Lam, and K.C. Ng, “An accurate active shape model
for facial feature extraction,”Pattern Recognition Letters, vol. 26, no. 15,
pp. 2409–2423, 2005.
[22] F. Destrempes, J. Meunier, M.F. Giroux, G. Soulez, and G. Cloutier,
“Segmentation in ultrasonic¡ emphasis,” MedicalImaging, IEEE
Transactions on, vol. 28, no. 2, pp. 215–229, 2009
[23] B.S. Garra D.G. Brown M.F. Insana, R.F. Wagner and T.H. Shawker,
“Analysis of ultrasound image texture via generalized
ricianstatistics,”Optical Engineering, vol. 25, no. 6, pp. 743–748,1986.
[24] P.M.Shankar. “A compound scattering pdf for the ultrasonic echo
envelope and its relationship to k and nakagami
distribution,”IEEETransactions,Ferroelect., Freq. Contol
[25] P.M. Shankar,“A general statistical model for ultrasonic scattering from
tissues,”IEEE Trans. Ultrasonic.Ferroelect., Freq. Contol
[26] Y.Meiry, YouTube, (2012, Jan 13), “Lecture 13 (Basics of MRI,
Ultrasound,”, [Video File]
http://www.youtube.com/watch?v=w3Ybm4A2GhI
[27] D.Kroon, University of Twente, Febuary2010., [Code]
[28] R. Werner, Allianz, Group Risk Controlling, (2006, May 1) [Code],
Germany
[29] J. L. Romeu, “The Chi-Square: a large Sample Goodness of Fit Test,”
Reliability Analysis Center, vol. 10, no. 4, pp. 1-6, START 2003-2004.</p>
@article{"International Journal of Medical, Medicine and Health Sciences:62374", author = "N. Mpofu and M. Sears", title = "Segmenting Ultrasound B-Mode Images Using RiIG Distributions and Stochastic Optimization", abstract = "In this paper, we propose a novel algorithm for
delineating the endocardial wall from a human heart ultrasound scan.
We assume that the gray levels in the ultrasound images are
independent and identically distributed random variables with
different Rician Inverse Gaussian (RiIG) distributions. Both synthetic
and real clinical data will be used for testing the algorithm. Algorithm
performance will be evaluated using the expert radiologist evaluation
of a soft copy of an ultrasound scan during the scanning process and
secondly, doctor’s conclusion after going through a printed copy of
the same scan. Successful implementation of this algorithm should
make it possible to differentiate normal from abnormal soft tissue and
help disease identification, what stage the disease is in and how best
to treat the patient. We hope that an automated system that uses this
algorithm will be useful in public hospitals especially in Third World
countries where problems such as shortage of skilled radiologists and
shortage of ultrasound machines are common. These public hospitals
are usually the first and last stop for most patients in these countries.
", keywords = "Endorcardial Wall, Rician Inverse Distributions,
Segmentation, Ultrasound Images.", volume = "7", number = "8", pages = "490-7", }
