Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0012—Biomedical image inspection
• • 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
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/60—Analysis of geometric attributes
  • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
• • 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
  • G06T2207/20112—Image segmentation details
  • G06T2207/20116—Active contour; Active surface; Snakes
• • 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 to a method for treating time sequence of volumetric images in the field of medical diagnosis.
• the system provides detecting and selecting, from anatomical images, an organ or part of it by means of segmentation and carrying out quantitative analysis for anatomical, function and perfusion studies.
• the invention relates to anatomical imaging for cardiac applications, such as in species MR images .
• the invention relates to segmentation and measurement steps on cardiac images, and in species the ventricular cavities and myocardium wall, starting from time sequence of MR images .
• the invention relates also to a method for automatic analysis of time/intensity curves to obtain relevant medical data and in species data on the perfusion of myocardium region corresponding to the time/intensity curves that is analyzed.
• the invention relates furthermore to a software means for automatic treatment of the images, the automatic filtering and modeling of such time/intensity curves relative to a time sequence of anatomical images.
• the images to treat can be of many tipi, and normally are images of an element, obtained with the various techniques such as ultrasonic pulses, PET, SPECT, Tom303 axial Computerizzata, MR, etc., which can be anatomical images , or images of function, obtained by means of time sequence of viste anatomiche of a particular zone of the organ, or still images of perfusion, obtained on the same organ after treatment of the paziente with the substances that mettono in risalto the perfusion in the organ.
• the images bidimensional can giving also volumetric images, acquired as slices (slices) in turn spatial.
• Uno of the points cardine in the processing of the images is the process of segmentazione: it consists in the suddividere the image in a series of regions homogeneous forn catalogare every single zone and accorpare among of loro the that correspond to the same tessuto. Segmentation allows then of determined regions homogeneous different for contour to different grey scale on which carry out with very easily measurezioni morfometriche very useful for diagnostica medica.
• I methods more used are those semiautomatic that allow to the operator of determined contour, values of threshold or of assegnare values to the single pixels interattiv noir with the elaboratore .
• the time of processing are very elevati, rendendo the lavoro to the utente very along and noioso, especially if the images to treat correspond to time sequences of data volumetric.
• I methods automatic presently in use are still estremêt inaffidabili, whereby the results obtained are difficilmente used in the lavoro of routine; it utilizzano of the criteri of segmentation of type statistico, which in any case usually is basano on models precostruiti for riconoscimento of regions of interest .
• EP0747004A2 Method of measuring ventricular volumes” eseguono, among the various functions, segmentation of anatomical images by means of the technique of the 'region growing' and operations of threshold.
• EP0747004A2 uses the results of segmentation for measure of the volumi ventricolari .
• the individuation of the contour with the technique of the 'region growing' is very sensitive to the dishomogeneousta in the grey scale of the image in particular in the zone of transition among two regions of interest .
• Tutte the operations described in the many methods are eseguite starting from grey scale images .
• this classe of images rientrano all the types of anatomical images.
• the dato digital corresponds to a value on a scale of grey scale.
• the approach more diffuso for studio of the perfusion myocardium is based on analysing the first passage of a bolo of contrast medium through the miocardium.
• two types of contrast medium intravascolari , which permangono in the vasi sanguigni for all the duration of the esame, and extracellulari , which in time brevi pass from the vasi to the spaces intracellulari .
• the measurection videodensitometrica of the regions of miocardium examined shows a variation time of the signal that riflette the concentration of the contrast medium in the tempo.
• time/intensity curves are ricavate from time sequences of images and descrivono the trend time of the grey scale of a zone spatial of the image, such curve is riferiscone to studies with the contrast medium both intervascular that extracellulare .
• time/intensity curves are subject to many types of noise such as for example: the noise heat, the noise due to fluttuazioni on the images generated by the insteadiness of the hardware of the machine of imaging such as for example the fluttuazioni of field permanent magnets statico and of the campi permanent magnets variable in MR, and the noise product from the meccanismi fisiologici, such as the heart beat and the attivita vasomotoria spontanea.
• the mancanza instruments appropriate for filtering and the extracting parameters from the time/intensity curves not allows an use correct of this metodica.
• a further object of the present invention is to provide a method for treating anatomical images which, when the images acquired correspond to time sequences, allows the time tracking of the analysis same.
• a particular object of the method concerning the anatomical images of myocardium, is that of selecting in completely automatic way both the inner and the outer wall of the left ventriculu during all the steps of the segmentation automatic cycle, allowing to make quantitative analysis , such as the calculus of the volume of the cavity and the thickness of the wall for all the cardiac cycle, as well as the calculus of the mass of the wall .
• Another particular object of the method is to allow the treatment of images of perfusion, executing an automatic segmentation like in the case of the anatomical images, varying specific parameters of the same software used for anatomical images .
• a method of treatment of the images has the characteristic that it comprises the steps of: - prefiltering the images by means of anisotropic nonlinear diffusion, - conditioning the prefiltered images by computing the temperature gradient vector flow, determined on the image of temperature gradient,
• an apparatus for quantitative analysis of data selected from anatomical images and/or of perfusion comprises, residenti in an elaboratore :
• the determination in automatic way of the time/intensity curves relative to a time sequence of images provides the steps of : - providing a grey scale images time sequence of an organ relating to the passage of a contrast medium and comprising a background image of the organ as displayed before the addition of the contrast medium;
• the step of filtering the average value of the grey scale is carried out by means of wavelet decomposition technique.
• the step of wavelet decomposition provides the steps of :
• the reconstructing step can be done by using all the bands of the signal and carrying out a thresholding step of the coefficients of the signal, the coefficients of the noise being of lower amplitude than those of the signal whereby at the end of the thresholding step the coefficients of the noise are eliminated.
• the reconstructing step is carried out by overlooking one or more frequency bands, said noise being found in one or more bands of the signal, whereby the elimination of such bands cleans the final signal from the noise.
• - a is the offset of the image, i.e. the grey scale without contrast medium; - f is proportional to the amplitude of the curve,
• - t 0 represents the time of delay between the iniection of the contrast medium and the display of its images
• - b is the slope of decrease of the same.
• a step is provided of extracting clinically useful indexes, comprising: slope or wash-in of the space interval of rise of the gamma curve; slope or wash-out of the curve in direct phase of decrease; maximum value of the curve; time corresponding to the maximum value of the curve; area closed under the curve; coefficient of correlation and coefficient of cross-correlation, which allows to estract data of relative perfusion, among different zones.
• a software means for filtering and analysing time/intensity curves obtained by anatomical images in presence of contrast medium comprises
• the means for filtering are based on the application of the wavelet decomposition technique.
• means are provided for fitting the filtered curves with a gamma function.
• Means may be provided for quantitative analysis of the gamma curve to obtain perfusion myocardium indexes.
• - figure 1 shows a diagrammatical view of the architecture of a software means that carries out the method according to the invention
• - figure 2A shows a the grey scale profile extracted according to the present invention by a bidimensional slice that highlights the contour of the endocardium and of the epicardium;
• figure 2B shows the determination steps, starting from bidimensional images, of the inner and outer contour of an organ, such as the contour of the endocardium and of the epicardium of figure 2A;
• FIG. 2C shows a diagrammatical view of the division in 16 zones of the contour of the endocardium and of the epicardium for calculus of time/intensity curves;
• FIG. 3 shows a flow- sheet of the software means that carries out the method according to the invention for analysis of anatomical data ;
• FIG. 4 shows a flow-sheet of the software means that carries out the method according to the invention for analysis of data of perfusion.
• figure 5A shows the profile of the image of temperature gradient computed according to the diagram of figure 3A;
• figure 5B shows the profile of the image of temperature gradient after the step of filtering anisotropic
• figure 5C shows the profile of the temperature gradient vector flow
• FIG. 7 shows a bloc diagram of an architecture software for determining, filtering and analysing time/intensity curves starting from the images of figures 2A, 2B and 2C;
• - figure 8 shows a block diagram of the steps of wavelet decomposition and following reconstruction of the signal of a time/intensity curve ;
• - figure 9 shows the comparison among a time/intensity curve not filtered and one filtered with the method according to the invention;
• FIG. 10 shows the profile of a gamma curve for fitting the time/intensity curves filtered. Description of a preferred embodiment
• a prototype of the present software has been implemented and is used for treating cardiac MR images and allows the analysis and the displaystion of images both anatomiche that of perfusion.
• the steps of prefiltering, segmentation and of outlet are based on the following conoscenze theoretical :
• Prefiltering impiega the operator of anisotropic nonlinear diffusion as described in "P. Perona, J. Malik. Scale-space and contour detection using anisotropic diffusion, IEEE Trans. On Pattern Analysis and Machine
• Condizionamento calcola the flow of the vettore GVF (Gradient Vector Flow) as described in "C. Xu and J.L. Prince. Snakes, Shapes, and gradient Vector Flow. IEEE Trans. On Image Process., pp. 359-369, 1998".
• GVF Gradient Vector Flow
• Segmentacade uses the snake as described in: "M.
• the block 1.1 represents a mass memory containing the images to analyse in DICOM 3 format .
• the formed DICOM 3 is a formed standard of archiviation for anatomical images, supportato from the more part of the machines of acquisiée .
• an esame medico comprises normally a succession of file DICOM 3, raphaving a volume spatial comprising a certain number of slices and that can be a succession time of the sopraddetti volumi spaziali.
• the block 1.2 include a converter that from the succession of images DICOM 3 derives the data of interest
• the block 1.3 allows the selection of the image to analyse in the steps following. in the step of inizializzation on the other handl automatic of the succession this block allows the introduction of the starting seed for definition of the contour.
• the block 2.1 represents an apparato that esegue a prefiltering the selected image block 1.
• This prefiltering consists in the application of an algorithm of anisotropic not linear diffusion.
• the anisotropic filtering carries out the reduction of the smal discontinuities (noise) in the image highlighting at the same time the contrast in the zone with high discontinuities (contour) .
• a first result of the anisotropic filtering is shown in figure 2A, where the contour SI of the endocardium, points 22 and 23, and S2 of the epicardium, points 21 and 24, are eshighti for allowing a treatment of the image in the way hereinafter described.
• the block 2.2 allows to modcel the parameters for anisotropic filtering with respect to the values standard defined in the block 1.
• the parameters modificabili are the number of iterations eseguite for causing the filtering and the value of the fixed of diffusione K.
• the block 3.1 represents an apparato that carries out the calculus of the temperature gradient mapping of the image.
• the temperature gradient mapping is computed by computing the spatial derivative in the two directions.
• the block 3.2 represents a device for computing the field GVF (Gradient Vector Flow) , capable of highlighting the brighter temperature gradientand of uniforming the regions with weakert temperature gradient .
• GVF Gradient Vector Flow
• the block 3.3 allows to modcalc the parameters for calculus of the GVF with respect to the values standard defined in the block 1.
• the parameter modificabile is the number of iterations eseguite for calculus .
• the block 4.1 represents an apparato for automatic detection of the inner contour SI of the ventricolo, starting from the images obtained after the further treatment of the image of which to figure 2A and shown in figure 5B.
• the detection is efollowed through the matching the contour SI of the ventricolo by a closed curve S (snake) .
• the fieldused for guidare the evolution time of the snake is the GVF computed in the block 3.2.
• the snake S is evolve being atspace interval from the minimum of the GVF field that coincide with the contour SI of the endocardium (punti 22 and 23 of figure 2A, 5A, 5B, 5C) .
• the seed S of partenza for detection of the contour can be product manually by the operator or automatically as verra specificato hereinafter.
• the block 4.2 represents an apparato for automatic detection of the outer contour S2 of the ventricolo.
• the starting seed is the snake SI computed in the block 4.1 raphaving the inner contour of the ventricolo.
• for definition of the outer contour S2 can be mounted an of the two methods described hereinafter.
• Lo snake SI is fact evolvere towards the maximum of the GVF field (punti 21 and 24 of figures 2A, 5A, 5B, 5C) that represents the contour of the epicardium. This is obtained cambiando the sign of the GVF field computed by the block 3.2 and repeating the step efollowed from the block 4.1.
• the snake SI defined byl block 4.1 is espanso radially with respect to its centre of a number of pixels according to the modalita of acquisition considerata, up to raggiungere the shape SI'.
• the zone in the snake thus defined of the temperature gradient mapping computed in the block 3.1 is made zero and the GVF field is again computed on the basis of the in the temperature gradient mapping. In this way the minimum of the in the
• GVF field correspondsra to the contour S2 of the epicardium.
• the snake espanso SI' is fact evolvere in according to the in the GVF field so that vada to measuring the outer contour S2 of the ventricolo.
• the block 5.1 represents an apparato for measuring of volume of the ventricular cavity on the basis of the inner contour defined byl block 4.1. On the basis of the data relative to the risolution of the images detected from the block 1.2 it is possible the calculus of the volume in mm3. from measurement of volume is therefore possible the calculus :
• the block 5.2 represents an apparato for effettuation of measuring on the ventricular wall, this wall is defined by the contour endocardic detected from the block 4.1 and from the contour epicardic detected from the block 4.2.
• the valutazioni of volume of the wall are carried out like to what described in the block 5.1. and thus possible the calculus:
• the block 5.3 represents an apparato for analysis of images of cardiac perfusion.
• the wall selected block 4 is split into 16 zone, as shown in figure 2C, starting from the centreide C of the snake that defines the contour endocardic (SI) detected in the block 4.1 is define a plurality of raggi, (i.e. 8 radial lines R1-R8) starting from this centreide and that pass for points all, a21 ... costituenti the contour endocardic.
• zone -2JL For each zone -2JL is computed the average value of the pixels contained in the zona . The process is repeated on all the images costituenti a perfusion time sequence. Is obtained thus the so called time/intensity curves for 16 zones considered. Furthermore is defined a zone of reference in the about of the centreide of the snake that defines the contour endocardiale and a further time/intensity curve is determined for this zone.
• apre tanti file DICOM 3 quante are the images relative to an analysis space/time and the organszza in a memory for images. Segue the selection 62 of an image to analyse to which is subject the following operations:
• the step of filtering can be evitata single when the curve are already ad high ratio signal/noise. In this case it is possible pass directly to the step of fitting with the gamma curve described hereinafter and extracting indici of utilita clinica.
• Lo instrument for determination works way following: - For each zone - 1_5_ of figure 2C is computed the average value of the grey scale relative to the points contained in the zone;
• sotspace interval the sfondo i.e. the corresponding average grey scale computed by the corresponding zone in the background image
• the time/intensity curves can bemixata as a curve of which the abscissa is the time sequence of the analysed images, whereas the ordinate is the average value of the grey scale relative to the zone (figure 6) .
• Lo instrument of filtering provides the application of the wavelet decomposition technique, this technique is used for decomposition in frequency of signals and has the advantage of scomporre the components of the signal in sottobande, each with the maximum risolution in frequency that the compete on the basis of the teorema of Nyquist. when the signal, i.e. the time/intensity curves, is sco prises in sottobande, is very more easy and affidabile working with the suitable instruments software in order to ripulire the signal from the rumori above described.
• the step of wavelet decomposition starts sottoponendo the samples of the signal 40 of the time/intensity curves to a step of filtering by means of two filters 41 and 42, respectively one low pass and the other high pass. This way are obtained two time sequences of samples chiamate respectively 'approximate signal ' 43 and 'detail signal' 44.
• the reconstructing step of the signal is carried out invertendo the process described and i.e. first is sovracampionano the coefficients of the signals dettagli 56, 57 ... and then is carries out again the step of filtering for all the different levels of decomposition.
• the reconstructed signal 50 is the sum of the approximation signal and of all the detail signals 53, 54, 56, 57 ....
• the step of sottocampionamento is demand since, owing to the step of filtering, the banda of the signal is dimezzata and the detail signals is troverebbero ad being sovracampionati with respect to the teorema of Nyquist.
• the sottocampionamento versus time allows to representsre in frequency the coefficients wavelet with the risolution frequenziale maximum confelt. from qui the characteristic main of the wavelet decomposition and i.e. the signal originale is decomprises in frequency bands each of which has the maximum risolution frequenziale.
• the reconstructing of the signal based on the process above described can be carried out in two modi :
• this step ipotizza that the coefficients of the noise are of lower amplitude than those of the signal and after the thresholding step such samples are eliminated.
• this method is ipotizza of being in presence of noise found in one or more bands of the signal, whereby the elimination of such bands cleans the final signal from the noise .
• the invention include a further step and i.e. the fitting the filtered curves with a gamma function described in literature.
• the gamma function is a curve computed that better defines the trend of the concentration of the intervascular contrast medium in the organ tissues (figure 10) .
• I five parameters present in the formula have the following significato: to represents the offset of the image, i.e.
• the gamma curve is not more suitable to dewritten this projecto.
• single the first part risente in way slight of this excici inerenti to this part can be used for analysis.
• a third step particular object of the invention relates to the extracting clinically useful indexes, such indici includono: slope of the space interval of rise of the gamma curve (known also as wash- in) ; slope of the curve in direct phase of decrease (wash-out) ; maximum value of the curve; time corresponding to the maximum value of the curve; area closed under the curve; extracting correlation indexes, such as coefficient of correlation and coefficient of cross-correlation, which allows to estract data of relative perfusion, among different zones.
• a prototype of the present software has been implemented and is used to control time/intensity curves obtained by time sequences of anatomical images for myocardium MR applications.

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