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Uterine Fibroid Diagnosis: A Dynamic Approach for Despeckling and Segmentation

M. Selvarani, S. Malarkhodi

Abstract


Uterine fibroids are the most common pelvic tumorsin females. Ultrasonic image segmentation is a difficult problem dueto speckle noise, low contrast, and local changes of intensity.Intensity-based methods do not perform particularly well onultrasound images. However, it has been previously the speckle noiseis removed by the Discrete Wavelet Transform (DWT). Then theseimages respond well to local Phase-Based Level Set methods (PBLS)which are theoretically intensity invariant. The proposed approachperformances are evaluated in terms of accuracy, specificity andsensitivity.


Keywords


Uterus, Fibroid, Ultrasound Imaging, Level Set,Local Phase, Speckle Noise, Discrete Wavelet Transform.

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References


Ahror Belaid, Djamal Boukerroui, Y.Maingourd, Jean-Francois Lerallut,‘Phase-Based Level Set Segmentation of Ultrasound Images’, IEEETransactions on Information Technology in Biomedicine, Vol. 15, No.

, January 2011.

Chitresh Bhushan, ‘Ultrasound Image Segmentation’, Basics on Image

Processing, Publised in April 15, 2009.

Hossein Rabbani, Mansur Vafadust, Purang Abolmaesum and Saeed

Gazor, ‘ Speckle Noise Reduction of Medical Ultrasound Images in

Complex Wavelet Domain Using Mixture Priors’, IEEE transactions on

Biomedical Engineering, Vol. 55, No. 9, September 2008.

Karan Sikkaa and Thomas M. Desernob, ‘Comparison of Algorithms for

Ultrasound Image Segmentation without Ground truth’, Basics on

Digital Image Processing, Publised in April 15, 2009.

LI Yueqin, LI Ping, CHEN Huimin, YAN Xiaopeng. ‘A speckle

Reduction and Image Enhancement Anisotropic Diffusion Method to

Underwater Ultrasonic Image Based on Wavelet Technology’,

International Symposium on Photo electronic Detection and Imaging

Related Technologies and Applications, Vol. 6625, 662511, 2007.

Mariana Carmen Nicolae, Luminiţa Moraru, Laura Onose, ‘Comparative

Approach for Speckle Reduction in Medical Ultrasound Images’,

National Conference of Biophysics, Vol. 20, No. 1, P. 13–21, October

Ratha Jeyalakshmi and Ramar Kadarkarai, “Segmentation and Feature

Extraction of Fluid-filled Uterine Fibroid-A knowledge-Based

Approach”, Maejo International Journal of Science and Technology Vol

(03), P.405-416, December 2010.

Richard Alan Peters, ‘A New Algorithm for Image Noise Reduction

Using Mathematical Morphology’, IEEE Transactions On Image

Processing. Vol. 4. No. 5. MAY 1995.

Sean Finn,Martin Glavin and Edward Jones, ‘Echocardiographic

Speckle Reduction Comparison’, IEEE Transactions on Ultrasonics,

Ferroelectrics, and Frequency Control, vol. 58, no. 1, January 2011.

Sriraam.N, Nithyashri.D, Vinodashri.L and Manoj Niranjan.P,

‘Detection of Uterine Fibroids Using Wavelet Packet Features with

BPNN Classifier’, 2010 IEEE EMBS Conference on Biomedical

Engineering & Sciences, December 2010.

Ujjwal Maulik, ‘Medical Image Segmentation Using Genetic

Algorithms’, IEEE Transactions on Information Technology In

Biomedicine, Vol. 13, NO. 2, MARCH 2009.

Vanithamani.R, Umamaheswari.G, ‘Performance Analysis of Filters for

Speckle Reduction in Medical Ultrasound Images’, International Journalof Computer Applications, Volume 12– No.6, December 2010.

Xiaoping Li Dong C. Liu, ‘Ultrasound Speckle Reduction Based onImage Segmentation and Diffused Region Growing’, Conference onInformation Sciences, 2008.

Xinbo Gao, Bin Wang, Dacheng Tao, Xuelong Li, ‘A Relay Level SetMethod for Automatic Image Segmentation’, IEEE Transactions OnSystems, Man, And Cybernetics, Vol. 41, No. 2, April 2011.

Yong Yue, Mihai M. Croitoru,Akhil Bidani, Joseph B. Zwischen berger,John W. Clark Jr, ‘Nonlinear Multiscale Wavelet Diffusion for SpeckleSuppersion and Edge Enhancement in Ultrasound Images’, IEEETransactions On Medical Imaging, Vol. 25, No. 3, March 2006.


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