WO1996038815A1 - Procede automatique de delineation des limites et de dimensionnement des regions par imagerie a accentuation de contrastes - Google Patents

Procede automatique de delineation des limites et de dimensionnement des regions par imagerie a accentuation de contrastes Download PDF

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Publication number
WO1996038815A1
WO1996038815A1 PCT/US1996/008257 US9608257W WO9638815A1 WO 1996038815 A1 WO1996038815 A1 WO 1996038815A1 US 9608257 W US9608257 W US 9608257W WO 9638815 A1 WO9638815 A1 WO 9638815A1
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WIPO (PCT)
Prior art keywords
border
pixel
recited
point
baseline
Prior art date
Application number
PCT/US1996/008257
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English (en)
Inventor
Harold Levene
Original Assignee
Molecular Biosystems, 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 Molecular Biosystems, Inc. filed Critical Molecular Biosystems, Inc.
Priority to JP8536746A priority Critical patent/JPH11506950A/ja
Priority to AU59629/96A priority patent/AU5962996A/en
Priority to EP96916909A priority patent/EP0829068A1/fr
Publication of WO1996038815A1 publication Critical patent/WO1996038815A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • 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
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • 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
    • G06T2207/30048—Heart; Cardiac

Definitions

  • the present invention relates in general to a method for processing ultrasound
  • ROIs regions of interest
  • SPECT single photon emission computed tomography
  • PET positron emission tomography
  • CT computed tomography
  • MRI magnetic resonance imaging
  • SPECT single photon emission computed tomography
  • PET positron emission tomography
  • MRI magnetic resonance imaging
  • angiography angiography
  • ROI region of interest
  • the ultrasonic transducer utilized is placed on a body surface overlying the area to be imaged, and sound waves are directed toward that area.
  • the transducer detects reflected sound waves and the attached scanner translates the data into video images.
  • the amount of energy reflected depends upon the frequency of the transmission and the acoustic properties of the substance. Changes in the substance's acoustic properties (e.g. variance in the acoustic impedance) are most prominent at the interfaces of different acoustic densities and compressibilities, such as liquid-solid or liquid-gas. Consequently, when ultrasonic energy is directed through tissue, organ structures generate sound reflection signals for detection by the ultrasonic scanner. These signals can be intensified by the proper use of a contrast agent.
  • contrast agents there are several types of contrast agents including liquid emulsions, solids, encapsulated fluids and those which employ the use of gas.
  • the latter agents are of particular importance because of their efficiency as a reflector of ultrasound. Resonant gas bubbles scatter sound a thousand times more efficiently than a solid particle of the same size.
  • These types of agents include free bubbles of gas as well as those which are encapsulated by a shell material.
  • Contrast enhanced images have the property that their presence in a particular
  • ROI produce a contrast visually recognizable from surrounding regions that are not suffused with the agent.
  • myocardial contrast echocardiography (“MCE").
  • MCE myocardial contrast echocardiography
  • the ejection fraction is a global measure of systolic function, while regional wall motion is a local measure.
  • EF ejection fraction
  • ESN is the end-systolic ventricular volume
  • the computerized image processing starts with a human operator selecting three image frames from a cardiac cycle: the opening end-diastolic frame, the end-systolic frame, and the closing end-diastolic frame. Once selected, the operator defines the endocardial and epicardial borders on each of the three selected frames. After the borders are defined for the first three frames, they are refined and the borders in the other frames from other points within the cardiac cycle are automatically determined by Geiser et al.'s process.
  • the ventricle may not be completely
  • contrast agent it is not likely that all areas are simultaneously opacified. For example, attenuation and the effects of shadowing may produce an image whereby one region of the left ventricle is at maximum brightness while, in other regions, no contrast is observed at all.
  • identification of the border region during the end-diastole or end-systole might lead to either an over or under estimation of the motion of the ventricle. If the ejection fraction or regional wall motion are over-estimated, the cardiologist might rule out a suspicion of ischemia, when it is in fact present. On the other hand, if the ejection fraction or regional wall motion are under-estimated, then the cardiologist might suspect ischemia where none is present and sent the patient on to a more expensive diagnostic procedure (e.g. angiography or nuclear imaging) or an expensive and invasive therapeutic procedure (e.g. angioplasty).
  • a more expensive diagnostic procedure e.g. angiography or nuclear imaging
  • an expensive and invasive therapeutic procedure e.g. angioplasty
  • the present invention is a novel system and method for automatically identifying borders of regions of interest within an image of a patient's organ or tissue. Initially, the operator of the system identifies a given set of images that will be taken for the system to
  • the set of the organ in question is the heart
  • images selected for analysis will usually be images that are taken at the same point in the
  • the system begins to generate images - before, during and after the administration of a contrast agent.
  • the system begins its automatic processing.
  • the steps of the processing include the identification of baseline image frames, identification of baseline intensities for each given pixel in the ROI, baseline subtraction on a per-pixel basis, determining a probability of signal-to-noise ratio for each pixel, and thresholding each pixel to determine if a pixel belongs to an area inside the border region or an area outside the border region.
  • the method refines the set by locally minimizing a total cost function that relates a low value to points typically found on a
  • Figure 1 depicts the manner in which ultrasound images are taken of a patient's heart by an ultrasound image processor that is used in accordance with the principles of
  • FIG 2 is a high level block diagram of one embodiment of an image processor unit that is used in accordance with the principles of the present invention.
  • Figures 3-7 depict a flow chart of the presently claimed border delineation method.
  • Figures 8(A) and 8(B) depicts how the present system may select candidate heart chamber border pixels.
  • the present invention encompasses general methods for the imaging and diagnosis of any patient tissues or organs capable of being imaged, the present description will be given from the standpoint of imaging the human heart. In many ways, the problems involved with imaging the human heart for purposes of border delineation and dimensioning are more difficult than with other organs.
  • the present description of the method for imaging the heart may then be simplified in order to image other patient organs and tissues that do not experience such difficulties.
  • the present invention should not be limited to merely for imaging the human heart; but encompasses all tissues
  • the present description is based upon administration of a contrast agent used with ultrasound imaging methodology.
  • the present invention should not be limited to merely ultrasound; but also encompasses other methodologies that may (or may not) use a contrast agent that is uniquely suited to that particular methodology.
  • Ultrasound methodology is described in greater detail in co-pending and co-assigned patent application Serial Number 08/428,723 entitled “A METHOD FOR PROCESSING REAL-TIME CONTRAST ENHANCED ULTRASONIC IMAGES", filed on April 25, 1995 by Levene et al., and herein incorporated by reference.
  • Ultrasound imaging systems are well known in the art. Typical systems are manufactured by, for example, Hewlett Packard Company; Acuson, Inc.; Toshiba America Medical Systems, Inc.; and Advanced Technology Laboratories. These systems are employed for two-dimensional imaging. Another type of imaging system is based on
  • ultrasound contrast agents are also well-known in the art. They include,
  • liquid emulsions solids
  • encapsulated fluids encapsulated
  • gaseous agents are of particular importance because of their efficiency as a reflector of ultrasound. Resonant gas bubbles scatter sound a thousand times more efficiently than a solid particle of the same size. These types of agents include free bubbles of gas as well as those which are encapsulated
  • the contrast agent may be administered via any of the known routes. These routes include, but are not limited to intravenous (IV), intramuscular (IM), intraarterial (IA), and intracardiac (IC).
  • IV intravenous
  • IM intramuscular
  • IA intraarterial
  • IC intracardiac
  • tissue or organ that receives a flow of blood may have images processed in the manner of the invention.
  • These tissues/organs may include, but are not limited to the kidneys, liver, brain, testes, muscles, and heart.
  • Short axis views may bisect the heart at different planes, at the level of the mitral valve, at the level of the papillary muscles, or at the level of the apex, for example.
  • the apical four chamber view with the transducer slightly tilted gives the five chamber view, where the aorta is visualized with the usual four chambers.
  • FIG. 1 a cut-away view of patient 30 attached to echocardiographic transducer 36 is shown. A transducer is placed on the patient,
  • Images may alternatively be acquired transthoracically or
  • An injection (34) of contrast agent is made into the patient's vein so that the contrast agent reaches the heart and interacts with the ultrasound waves generated by transducer 36. Sound waves reflected and detected at transducer 36 are sent as input into image processing system 38.
  • image processing system As the contrast agent enters into various heart regions, image processing system
  • Tissue areas that do not brighten when expected may indicate a disease condition in the area (e.g. poor or no circulation, presence of thrombus, necrosis or the like).
  • Image processing system 38 comprises diagnostic ultrasound scanner 40, optional analog-to-digital converter 42, image processor 44, digital-to-analog converter 56, and color monitor 58.
  • Ultrasound scanner 40 encompasses any means of radiating ultrasound waves to the region of interest and
  • Scanner 40 could comprise transducer 36 and a means of producing electrical signals in accordance with the reflected waves detected. It will be appreciated that such scanners are well known in the art.
  • the electrical signals generated by scanner 40 could either be digital or analog. If
  • the signals are digital, then the current embodiment could input those signals into image processor 44 directly. Otherwise, an optional A/D converter 42 could be used to convert
  • Image processor 44 takes these digital signals and processes them to provide video images as output.
  • the current embodiment of image processor 44 comprises a central processing unit 46, trackball 48 for user-supplied input of predefined regions of interest, keyboard 50, and memory 52.
  • Memory 52 may be large enough to retain several video images and store the border delineation method 54 of the present invention.
  • CPU 44 thus analyzes the video images according to stored border delineation method 54.
  • D/A converter 56 After a given video image is processed by image processor 44, the video image is output in digital form to D/A converter 56. D/A converter thereby supplies color monitor 58 with an analog signal capable of rendering on the monitor. It will be appreciated that the present invention could alternatively use a digital color monitor, in which case D/A converter 56 would be optional.
  • FIGS 3-7 are flowcharts describing the border delineation method as currently embodied. The method starts at step 100 with the operator selecting a point of interest in the cardiac cycle where
  • grey scale (not contrast-enhanced) ultrasound imaging is started at step 102.
  • a decision is made as to whether to process the current image. If the image is at the point of interest in the cardiac cycle, then the image is processed at steps 104 thru 108. Otherwise , it is not processed.
  • Noncontrast enhanced imaging is continued until a sufficient number of initial baseline images are taken at step 110. These initial images, together with later images taken after the contrast agent has "washed out", form the basis of the entirety of the baseline images.
  • a contrast agent is administered to the patient at step 114 and "washes into” the chambers of the heart, first, then slowly perfuses into the tissues of the heart muscles themselves. The images are then captured at the selected point(s) in the cardiac cycle until the contrast agent is no longer present in the heart's chamber at steps 116 thru 122. This could be determined by selecting a "trigger" region of interest (T-ROI) that is used to identify whether the contrast agent is the heart chamber. A most advantageous T-ROI to be selected would be somewhere in the heart chamber because the heart chamber receives the contrast agent prior to perfusion in the heart muscle.
  • T-ROI region of interest
  • image motion correction is performed to improve the quality of the images at step 128. This may be done either manually or in an automated fashion. If done manually, for example, the operator would indicate on each image to what extent and in what direction one image would need to move to register with a reference image. Such a manual method is described in "Digital Subtraction Myocardial Contrast Echocardiography: Design and Application of a New Analysis Program for Myocardial Perfusion Imaging," M. Halmann et al., J. Am. Soc. Echocardiogr. 7:355-362 (1994).
  • the operator After motion correction is performed, the operator then preselects a general region of interest on a given frame in order to give the process in an initial region for which to locate the border thereof at step 130. This may be accomplished by having the operator circle the region of interest with a light pen on an interactive video screen or by
  • This selected region is used only to restrict the search area for the endocardium border in order to reduce the processing time.
  • a properly selected region should include the left ventricle surrounded by myocardial tissue. The analysis then begins on each pixel within the ROI.
  • the set of true baseline frames are selected from the set of initial, pre-contrast frames and the set of post-contrast frames. Steps 134, 136, and 138 depict three different ways in which this set may be formed. First, the operator could manually select all of the baseline frames. Second, the operator identifies an area clearly within the left ventricle
  • the standard deviation of the pixel intensity is calculated. For any given pixel, the data points over time are compared against the computed standard deviation in step 144. If the pixel intensity is within the standard deviation for a putative baseline value, then the pixel data point is considered a baseline value.
  • the pixel data point is outside the standard deviation, and the data point is removed from any further consideration at step 146.
  • the linear regression analysis is then re-calculated, including the standard deviation. This defines an iterative process for each pixel over time.
  • the pixels of the chamber are determined, at step 148. By clearly identifying the pixels of the chamber, the method may then discard these pixels from further consideration in delineating the border pixels.
  • the first step to accomplishing this goal is baseline subtraction. For each pixel in the ROI, another linear regression analysis is performed on the baseline pixel intensity over time at step 152. This provides a linear best-fit curve having a derived slope and intercept at step 154. For each non-baseline frame occurring at a given time, ti, the baseline intensity is derived from the linear curve as occurring for that particular time. The baseline value is then subtracted from the non-baseline pixel intensity at step 156.
  • the signal derived solely from the contrast, Si is determined.
  • the observed pixel intensity may have decreased to an extent to be less than the estimated baseline intensity. In such a case, Si is taken to be zero.
  • a composite signal-to-noise ratio (S/N) is determined from the signal, Sk, and the signals from the temporally adjacent heart cycles, Sk-i and Sk+i- A peak signal may arise from spurious noise, so that the signals are weighted according to the equation:
  • ROC characteristic characteristic
  • the signal-to-noise ratio is then treated as a standardized, normal variable and the
  • P[(S/N) k ] may be calculated as follows:
  • the maximum signal-to-noise ratio over the non-baseline frames is determined. Because there is a
  • a probability threshold may be established distinguishing the two regions, with probabilities above the threshold identifying pixels in the myocardium and probabilities below the threshold identifying pixels in the left ventricle. This comparison is accomplished at step 164 and continues until all the pixels i the ROI have been analyzed.
  • cost weighting is used. In that case, if a small area within the chamber near the border is misclassified, an edge detection method will have
  • the center of mass of the ventricle pixels, (xi, y ), is then determined at step 172 and referred to as the center of the left ventricle:
  • m is the number of ventricle pixels.
  • the envelope (or border) of the ventricle pixels is now determined from the binary image.
  • the ventricular pixels are searched to find the points that have the minimum and maximum y value and the minimum and maximum x value - thus, defining a maximum of four points. It should be appreciated that the orientation of the images is not important. At each of these four locations, there may be one or more points; it is most convenient to pick a location with only one point, but is not necessary. In the case of all
  • any one of the points at any of the locations will suffice as the reference point of step 178.
  • the point is identified as the first point belonging to the border and it serves as the starting point of the envelope tracing method.
  • the envelope is traced by determining which adjacent point
  • the angle, 1, of the reference point, (X 2 , Y 2 ), is determined as follows:
  • Figures 8 A and 8B depict the selection of candidate border points in the myocardium.
  • Figure 8 A shows a color picture of a heart chamber (colored red in the Figure) surrounded by the dark myocardium.
  • Figure 8B shows an enlarged view of the region in Figure 8 A that is bordered by the white box.
  • the border is gradually and automatically filled out (as depicted as the white solid curve).
  • the last border point selected is depicted as the white circle. From this last border point, the radial lines are sent out to help determine the next border point.
  • a candidate border point is found along each radial line, with the ventricular pixel nearest to the reference point chosen. Radial lines are radiated out over 180 degrees , from 1 to 1 + 180 degrees. The cost function is then calculated for each candidate point. If the cost of all points is above a threshold cost, then the angular range of radial lines is increased. The candidate point with the lowest cost is chosen as the adjacent border point and
  • the cost function may have global and local factors. Global factors, for example, may emphasize a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors emphasize regional border characteristics.
  • the cost factors may have global and local factors. Global factors, for example, may emphasize a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors emphasize regional border characteristics.
  • the cost factors may have global and local factors. Global factors, for example, may emphasize a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors emphasize regional border characteristics.
  • the cost factors may have global and local factors. Global factors, for example, may emphasize a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors emphasize regional border characteristics.
  • the cost factors may have global and local factors. Global factors, for example, may emphasize a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors emphasize regional border characteristics.
  • the cost factors may have global and local factors. Global factors, for
  • G(ps) may be determined using the Sobel operators as defined as
  • the cost factor, c 2 for the candidate point is:
  • the angle of the gradient of the pixel intensity about the border should be slowly changing for a smooth contour.
  • the cost factor, c 3 for the candidate point is given as:
  • > r is the angle of the gradient at the reference point
  • > c is the gradient angle for the candidate point
  • > is the angle between a line from the reference point to the center of the ventricle and the candidate point.
  • the background of the summary image may consist of an average of the baseline frames. Superimposed upon this background is the border, which may be highlighted in a different color.
  • a possible format to display the border is depicted in Figure 8B as the solid white border line. The border is thus shown as the continuous broad white band that encloses the left ventricle chamber.

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Abstract

Système et procédé nouveaux permettant d'identifier automatiquement les limites des régions visées à l'intérieur d'une image d'un organe ou d'un tissu d'un patient. Le système produit des images avant, pendant et après l'administration d'un agent de contraste. Une fois l'ensemble d'images recueillies, le système lance un traitement automatique des images. Les étapes de ce traitement comporte l'identification des trames d'image de ligne de base, l'identification des intensités de ligne de base pour chaque pixel de la région visée, la soustraction effectuée par pixel, la détermination d'une probabilité de rapport signal-bruit pour chaque pixel et le seuillage de chaque pixel en vue de déterminer si un pixel appartient à une zone située à l'intérieur de la région frontière ou à une zone située à l'extérieur de la région frontière. Pour déterminer exactement quels sont les pixels situés à la limite, le système opère une discrimination sur l'ensemble d'images par une minimisation locale d'une fonction de pondération attribuant une valeur basse aux points trouvés généralement sur une image à accentuation de contraste. La limite de la région visée est ainsi déterminée.
PCT/US1996/008257 1995-05-31 1996-05-30 Procede automatique de delineation des limites et de dimensionnement des regions par imagerie a accentuation de contrastes WO1996038815A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP8536746A JPH11506950A (ja) 1995-05-31 1996-05-30 コントラスト強調撮像を用いた自動境界線引きおよび部位寸法記入
AU59629/96A AU5962996A (en) 1995-05-31 1996-05-30 Automatic border delineation and dimensioning of regions usi ng contrast enhanced imaging
EP96916909A EP0829068A1 (fr) 1995-05-31 1996-05-30 Procede automatique de delineation des limites et de dimensionnement des regions par imagerie a accentuation de contrastes

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US45583595A 1995-05-31 1995-05-31
US08/455,835 1995-05-31

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Publication number Priority date Publication date Assignee Title
GB2329313A (en) * 1997-07-02 1999-03-17 Yumi Tomaru Selecting region of interest in renal scintigraphy
GB2329313B (en) * 1997-07-02 1999-08-04 Yumi Tomaru Method of semi-automated selecting renal region of interest in scintigraphy
US7630529B2 (en) 2000-04-07 2009-12-08 The General Hospital Corporation Methods for digital bowel subtraction and polyp detection
GB2370441A (en) * 2000-08-21 2002-06-26 Leica Microsystems Automatic identification of a specimen region in a microscope image.
GB2370441B (en) * 2000-08-21 2003-11-19 Leica Microsystems Control of an analytical operation and/or adjustment operation in a microscope system
US7376253B2 (en) 2001-02-13 2008-05-20 Koninklijke Philips Electronics N.V. Analysis of successive data sets
US20100316270A1 (en) * 2007-12-20 2010-12-16 Koninklijke Philips Electronics N.V. 3d reconstruction of a body and of a body contour
US11107587B2 (en) 2008-07-21 2021-08-31 The Board Of Trustees Of The Leland Stanford Junior University Method for tuning patient-specific cardiovascular simulations
US8200466B2 (en) 2008-07-21 2012-06-12 The Board Of Trustees Of The Leland Stanford Junior University Method for tuning patient-specific cardiovascular simulations
US10354050B2 (en) 2009-03-17 2019-07-16 The Board Of Trustees Of Leland Stanford Junior University Image processing method for determining patient-specific cardiovascular information
US9268902B2 (en) 2010-08-12 2016-02-23 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US10441361B2 (en) 2010-08-12 2019-10-15 Heartflow, Inc. Method and system for image processing and patient-specific modeling of blood flow
US8311750B2 (en) 2010-08-12 2012-11-13 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US8315812B2 (en) 2010-08-12 2012-11-20 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US8315814B2 (en) 2010-08-12 2012-11-20 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US8315813B2 (en) 2010-08-12 2012-11-20 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US8321150B2 (en) 2010-08-12 2012-11-27 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
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