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UW-Madison ECE 533 - Despeckle Filtering in Medical Ultrasound Imaging

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Despeckle Filtering in Medical Ultrasound ImagingIntroductionNoise StatisticsFiltering MethodsTest ImagesTest Images (Cont’d)Method 1: Wiener FilterWiener Filter: Noise Power SpectrumWiener Filter ResultsWiener Filter Results (Cont’d)Slide 11Method 2: Anisotropic Diffusion FilterMethod 2: Anisotropic Diffusion Filter (Cont’d)Anisotropic Diffusion Results:Anisotropic Diffusion Results: (Cont’d)Slide 16Comparison of Image ProfileMethod 3: K-distribution Based Adaptive FilterK DistributionAdaptive filter for uncompressed imagesAdaptive filter for log-compressed imagesAdaptive Filter results:Adaptive Filter results: (Cont’d)Method 4: Wavelet FilterWavelet FilterWavelet Filter Results:Wavelet Filter Results: (Cont’d)Comparison of Filter PerformanceConclusionDespeckle Filtering in Medical Ultrasound Imaging Hairong Shi (1) Xingxing Wu (2)(1)DepartmentofMedicalPhysics,UniversityofWisconsin-Madison(2)DepartmentofElectricalandComputerEngineering,UniversityofWisconsin-MadisonIntroductionThe medical Ultrasound B-scan image is acquired by summation of the echo signals from locally correlated scatterers in beam range. Locally correlated multiplicative noises from small scatterers corrupt ultrasound image. These noises are commonly called “speckles”.In many cases the speckle noise degrades the fine details and edge definition, limits the contrast resolution, limits the detect ability of small, low contrast lesions in body. And it should be filtered out.Noise StatisticsFor research purpose,Radio-frequency (RF) data are collected. To show B-mode image, RF data are first envelope detected, and then logarithm compressed.The multiplicative speckle is converted into additive noise after logarithm compression, the noise is spatially correlated, and has a Rayleigh amplitude PDF:  0,2exp222 aaaapAFor fully developed speckle magnitude, the mean to standard deviation-pointwise SNR=1.9 (5.58dB)Filtering MethodsIn this project, we implement 4 filtering methods:(1) Wiener Filter;(2) Anisotropic Diffusion Filter;(3) Wavelet Filter;(4) Adaptive Filter;Test ImagesWe use the following test images to evaluate the performance of the filters.(1) 4 simulated inclusion phantoms with different contrast. Center frequency 3MHz, band width 40%, no attenuation. Contrast 10dB, 5dB, -5dB and -10dB. cmcm10dB0 2 4 601234567cmcm5dB0 2 4 601234567cmcm-5dB0 2 4 601234567cmcm-10dB0 2 4 601234567Test Images (Cont’d)(2) An in-vitro B-mode image for a plaque from human carotid artery. The plaque is embedded in gelatin. From Aloka SSD2000 Medical Ultrasound system.cmcmCarotid Artery Plaque0 0.5 1 1.5 2 2.5 300.511.522.53Method 1: Wiener FilterSince the input filter g=1 in frequency domain, the Wiener filter is:The power spectrum of the underlying image is modeled as:Where σs2 can be replaced by the mean variance of the noised image σx2. μx and μy are frequency coordinators, the range is [-π, π). wwssssSSSW2222yxsssSWiener Filter: Noise Power SpectrumThe Power Spectrum of speckle pattern Sww is averaged from 12 simulated speckle patterns with image size 128*128. 0.511.522.5x 106xyPower Spectrum of Noise-3 -2 -1 0 1 2 3-3-2-10123Wiener Filter Results406080100120140Weiner Filter restored image, inclusion 10dBcmcm0 2 4 60123456730405060708090100110120Weiner Filter restored image, inclusion 5dBcmcm0 2 4 60123456730405060708090100110Weiner Filter restored image, inclusion -5dBcmcm0 2 4 60123456730405060708090100110Weiner Filter restored image, inclusion -10dBcmcm0 2 4 60123456710dB5dB-5dB -10dBThe restored images by Wiener filter are excellent:(1) Most speckles are removed;(2) Inclusions are clearly seen. even for 5dB contrast cases(3) The background is uniform as we simulated. The main reason is that the averaged power spectrum of the noise is very close to the noise power in the noised images, so we can restore images well.Wiener Filter Results (Cont’d)Plaque SamplecmcmCarotid Artery Plaque After W iener Filter0 0.5 1 1.5 2 2.5 300.511.522.53Wiener Filter Results (Cont’d)The power spectrum of simulated noise can be applied well onto the real B-mode images:(1) The speckles are also removed efficiently(2)The structure of the materials are restored. There are still some speckles in restored images, which means the simulated noise power spectrum is not perfectly matched with the real ones. The rest speckles can be removed by median filters.The image qualities can be improved by unsharp mask and histogram stretch.Method 2: Anisotropic Diffusion FilterAnisotropic diffusion is an efficient nonlinear technique for simultaneously performing contrast enhancement and noise reduction. It smoothes homogeneous image regions and retains image edges. The main concept of Anisotropic diffusion is diffusion coefficient. Perona and Malik (1990) proposed 2 options:Or   00 ItIIIcdivtI  2/11kxxc    2/exp kxxc Method 2: Anisotropic Diffusion Filter (Cont’d)The anisotropic diffusion method can be iteratively applied to the output image:Parameter k~[20,100], step sizeλ<=0.25.                  nSouthnSouthnWestnWestnEastnEastnNorthnNorthnnIIcIIcIIcIIcII1Anisotropic Diffusion Results:20406080100120140cmcmInclusion 10dB. Iteration Step: 100 k= 50 l= 0.25 method: 10 2 4 60123456720406080100120cmcmInclusion 5dB. Iteration Step: 100 k= 50 l= 0.25 method: 10 2 4 601234567102030405060708090100110cmcmInclusion -5dB. Iteration Step: 100 k= 50 l= 0.25 method: 10 2 4 601234567102030405060708090100110cmcmInclusion -10dB. Iteration Step: 100 k= 50 l= 0.25 method: 10 2 4 60123456710dB5dB-5dB -10dBThe anisotropic diffusion filter can restore noised image well:(1)Speckles are removed and inclusions show clearly. (2)In Anisotropic diffusion method, we don’t need know the noise pattern or power spectrum, this is the advantage over Wiener filter. The anisotropic diffusion method needs more computation time than Wiener Filter method. Parameter selection, iteration loop selection all affect the final results.Anisotropic Diffusion


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UW-Madison ECE 533 - Despeckle Filtering in Medical Ultrasound Imaging

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