EP2807628A1 - Système et procédé de restauration d'image - Google Patents

Système et procédé de restauration d'image

Info

Publication number
EP2807628A1
EP2807628A1 EP13741583.2A EP13741583A EP2807628A1 EP 2807628 A1 EP2807628 A1 EP 2807628A1 EP 13741583 A EP13741583 A EP 13741583A EP 2807628 A1 EP2807628 A1 EP 2807628A1
Authority
EP
European Patent Office
Prior art keywords
imaging system
image
quality
improving
system image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP13741583.2A
Other languages
German (de)
English (en)
Other versions
EP2807628A4 (fr
Inventor
Said Benameur
Frédéric LAVOIE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Eiffel Medtech Inc
Original Assignee
Eiffel Medtech Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eiffel Medtech Inc filed Critical Eiffel Medtech Inc
Publication of EP2807628A1 publication Critical patent/EP2807628A1/fr
Publication of EP2807628A4 publication Critical patent/EP2807628A4/fr
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20052—Discrete cosine transform [DCT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

Definitions

  • the present disclosure relates to an image restoration system and method. More specifically, the present disclosure relates to an image restoration system and method for restoring images obtained from an ultrasound imaging system.
  • ultrasound imagery is currently considered to be a non-invasive, portable, non- expensive and safe (for the patient and operator) visualization medical tool for investigating biological tissues of a body.
  • imaging modality i.e. low signal-to-noise ratio, low resolution and contrast
  • artifacts due to the speckle noise effect that drastically deteriorates image quality and sometimes makes imperceptible clinically important details within these images (such as contours of anatomical structures).
  • the present disclosure provides a system for improving the quality of an imaging system image, comprising:
  • an input/output interface configured to receive the imaging system image
  • processor in communication with the input/output interface, the processor being configured to:
  • step b) includes the sub- steps of:
  • the present disclosure also provides a corresponding method for improving the quality of an imaging system image as well as a processor executable product stored on a data storage medium, configured to cause the processor to perform operations corresponding to the method for improving the quality of an imaging system image.
  • FIG. 1 is a schematic representation of an image restoration system in accordance with an illustrative embodiment of the present disclosure
  • FIG. 2 is a schematic representation of an image restoration system in a remote usage configuration
  • FIG. 3 is a flow diagram of an image restoration process in accordance with an illustrative embodiment of the present disclosure
  • FIGS. 4A and 4B are ultrasound images of a distal femur showing the medial side, coronal plane (FIG. 4A) and the medial posterior condyle, axial plane (FIG. 4B);
  • FIGS. 5A and 5B show the modulus of H"(u,v) after application of the discrete cosine transform (DCT)-based denoising step to the images of FIG. 4A and FIG. 4B, respectively;
  • DCT discrete cosine transform
  • FIGS. 8A and 8B are deconvolved images corresponding to FIG. 4A and FIG. 4B, respectively.
  • the non-limitative illustrative embodiment of the present disclosure provides a system and a method for improving the quality of images obtained from an imaging system, such as an ultrasound imaging system, through the application of an image restoration process in order to recover clinically important image details, which are often masked due to resolution limitations.
  • PSF Point Spread Function
  • ML Maximum Likelihood
  • the image restoration system 10 includes a processor 12 with an associated memory 14 having stored therein processor executable instructions 16 for configuring the processor 12 to perform various processes, namely image restoration process, which process will be further described below.
  • the image restoration system 10 further includes an input/output (I/O) interface 18 for communication with an imaging system 20 and a display 30.
  • I/O input/output
  • the image restoration system 10 obtains images, for example ultrasound images, from the imaging system 20 and executes the image restoration process 16 on the acquired images.
  • the resulting restored images are then displayed on the display 30 and may be saved to the memory 14, to other data storage devices or medium 40, or provided to a further system via the I/O interface 18.
  • the image restoration system 10 may be remotely connected to one or more imaging systems 20 and/or remotely operated through a remote station 62 via a wide area network (WAN) such as, for example, Ethernet (broadband, high-speed), wireless WiFi, cable Internet, satellite connection, cellular or satellite network, etc.
  • WAN wide area network
  • the remote station 62 may also have associated data storage devices or medium 64 for locally storing restored images provided by the image restoration system 0.
  • FIG. 3 there is shown a flow diagram of an illustrative example of the image restoration process 100 executed by the processor 12 (see FIG. 1). Steps of the process 100 are indicated by blocks 102 to 110.
  • the process 100 starts at block 102 where an image, for example an ultrasound image, is obtained from the imaging system 20 and, at block 104, subdivided.
  • an image for example an ultrasound image
  • a deconvolution factor is determined for the image and, at block 108, the deconvolution factor is applied to the subdivided image resulting in a restored image.
  • the restored image is provided, for example through the display 30 and/or stored in a data storage device or medium 40.
  • the PSF happens to exhibit spatial dependency due, among other things, to the non-uniformity of focusing, the dispersive attenuation and the heterogeneity of the different interrogated tissues. Nevertheless, a relatively low spatial variability of these phenomena makes it possible to divide the obtained acoustic image into a predefined number of small enough (possibly overlapping) images, for which the data within each such smaller image can be considered to be quasi-stationary, with a different PSF. It is then assumed that, the entire image can be easily recovered by combining all the local results obtained in this manner.
  • Equation 1 is more easily described in frequency domain as a simple product and sum where the capital letters indicate the Fourier transforms of the corresponding spatial functions:
  • the log-spectrum of the degraded ultrasound image (amplitude and phase) is considered to be a noisy version of the complex log-spectrum of the PSF to be estimated and in this setting, in which log
  • the DCT-based denoising procedure consists in applying iteratively, until a maximal number of iterations is reached or until convergence is achieved, frequential filtering based on the DCT transform of each 8 ⁇ 8 sub-image extracted from the current version of the image to be denoised (initially, this current image estimate is the noisy image itself).
  • the easily-impiemented hard thresholding rule [13] is used, also classically used in wavelet based denoising approaches, where e is a threshold level and w is one of the coefficients obtained by the DCT transform of the block (of size 8 x 8 pixels) extracted from the current image to be denoised,
  • this transform is made translation-invariant, by using the DCT of all (circularly) translated version of each channel of the image (herein assumed to be toroidal) [14] (this implies computing a set of 8 horizontal shifts and 8 vertical shifts transformed images) which is then averaged at each step of this iterative denoising procedure.
  • This iterative denoising procedure is applied on the noisy version of log H(u,v), i.e., log G(u,v) (amplitude and phase) and allows us to obtain a first rough estimate of log f-T(u,v) which will be refined in the next step.
  • Wimrd E O if ⁇ w ⁇ ⁇ , w otherwise
  • the estimation method now relies on an additional constraint, namely that the PSF to be estimated has the following parametric form: cos(2?r/»
  • [0040] which is the PSF model used in [15], i.e. asymmetric (across the x-axis and y-axis) cosine modulated by a Gaussian envelope whose the Fourier spectrum, i.e. its MTF (in fact a band-pass filter), namely H(u,v) can be written in the Fourier domain:
  • H(u, v) na s a y exp(-2 rV 2 ) ⁇ exp(-2 W y (v-/ 0 ) 2 ) + ⁇ ⁇ (-2 2 ⁇ ( ⁇ + / 0 ) 2 ) ⁇
  • This 2-component Gaussian mixture model is estimated thanks to a E -based clustering algorithm [11] ⁇
  • the initial parameters of this iterative procedure are given by the ML estimation on the partition given by a simple K-means clustering procedure.
  • FIGS. 4A and 4B show the original ultrasound images of the distal femur, more specifically the medial side, coronal plane (FIG. 4A) and the medial posterior condyle, axia! plane (FIG. 4B)
  • FIGS. 5A and 5B show the modulus of H " (u,v) after application of the DCT-based denoising step to the images of FIG. 4A and FIG. 4B, respectively. It can be seen that two different pass-band filters, related to two different PSFs are visible on these images. It can also be seen that there is no aliasing error and this first denoising step allowing the obtainment of the expected shape of a band-pass filter (see Equation 5) on which the learning step of the Gaussian mixture, exploiting the EM procedure, will be achieved. The Gaussian mixture, estimated from these two spectrum data by the EM algorithm (without the additional constraint of symmetry) is shown in FIGS. 6A and 6B. Two examples of PSF estimation with the present approach are presented in FIGS. 7A to 7D. Finally, FIGS. 8A and 8B show examples of deconvolution ultrasound images using the deconvolution scheme presented herein.
  • PSF point-spread function
  • FIGS. 7A to 7D are estimated spectrums of the point- spread function (PSF) corresponding to FIG. 4A (FIGS. 7 A and 7C) and FIG. 4B (FIGS. 7B and 7D), and FIGS. 8A and 8B are deconvolved images corresponding to FIG. 4A and FIG. 4B, respectively.
  • PSF point- spread function
  • Taxt, T. "Restoration of medical ultrasound images using two-dimensional homomorphic deconvolution," IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 42, 543 554 (July 1995).

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Pathology (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Radiology & Medical Imaging (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Biophysics (AREA)
  • Image Processing (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

L'invention concerne un système et un procédé permettant d'améliorer la qualité d'images ultrasonores. Le système comprend un processeur étant configuré pour subdiviser l'image ultrasonore, pour déterminer un facteur de déconvolution pour l'image ultrasonore et pour appliquer le facteur de déconvolution à l'image ultrasonore subdivisée, ce qui permet de fournir une image restaurée.
EP13741583.2A 2012-01-23 2013-01-23 Système et procédé de restauration d'image Ceased EP2807628A4 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201261632340P 2012-01-23 2012-01-23
CA2765244A CA2765244A1 (fr) 2012-01-23 2012-01-23 Systeme et procede de regeneration d'image
PCT/CA2013/000057 WO2013110174A1 (fr) 2012-01-23 2013-01-23 Système et procédé de restauration d'image

Publications (2)

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EP2807628A1 true EP2807628A1 (fr) 2014-12-03
EP2807628A4 EP2807628A4 (fr) 2015-11-25

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EP (1) EP2807628A4 (fr)
CA (2) CA2765244A1 (fr)
WO (1) WO2013110174A1 (fr)

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KR20210107096A (ko) 2018-12-27 2021-08-31 엑소 이미징, 인크. 초음파 이미징에 있어서 감소된 비용, 사이즈, 및 전력으로 이미지 품질을 유지하는 방법

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ATE531006T1 (de) * 2008-01-29 2011-11-15 Bergen Teknologioverforing As Iterative blindentfaltung unter nebenbedingungen

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CA2765244A1 (fr) 2013-07-23
WO2013110174A8 (fr) 2013-12-12
EP2807628A4 (fr) 2015-11-25
WO2013110174A1 (fr) 2013-08-01
CA2861126C (fr) 2020-11-10
CA2861126A1 (fr) 2013-08-01

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