WO2004082453A2 - Determination de lesions dans une image - Google Patents

Determination de lesions dans une image Download PDF

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Publication number
WO2004082453A2
WO2004082453A2 PCT/DK2004/000188 DK2004000188W WO2004082453A2 WO 2004082453 A2 WO2004082453 A2 WO 2004082453A2 DK 2004000188 W DK2004000188 W DK 2004000188W WO 2004082453 A2 WO2004082453 A2 WO 2004082453A2
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information
image
lesion
lesions
probability
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PCT/DK2004/000188
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English (en)
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WO2004082453A3 (fr
Inventor
Niels Vaever Hartvig
Jean-Marc Ferran
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Retinalyze Danmark A/S
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    • 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
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/0016—Operational features thereof
    • A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/12—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
    • 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
    • G06T2207/20152—Watershed segmentation
    • 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
    • G06T2207/30041—Eye; Retina; Ophthalmic
    • 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
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention relates to a method for assessing the presence or absence of lesion(s) in an image from an individiual and a system therefor, wherein said image may be any image potentially comprising lesions, in particular an image from medical image diagnostics, and more particularly an ocular fundus image.
  • the probability of the lesion(s) is corrected with information from said individual.
  • Fundus image analysis presents several challenges, such as high image variability, the need for reliable processing in the face of nonideal imaging conditions and short computation deadlines. Large variability is observed between different patients - even if healthy, with the situation worsening when pathologies exist. For the same patient, variability is observed under differing imaging conditions and during the course of a treatment or simply a long period of time. Besides, fundus images are often characterized by having a limited quality, being subject to improper illumination, glare, fadeout, loss of focus and artifacts arising from reflection, refraction, and dispersion.
  • vascular tree of fundus images is an important task in fundus image analysis for several reasons.
  • First of all the vascular tree is the most prominent feature of the retina, and it is present regardless of health condition. This makes the vascular tree an obvious basis for automated registration and montage synthesis algorithms.
  • the task of automatic and robust localization of the optic nerve head and fovea, as well as the task of automatic classification of veins and arteries in fundus images may very well rely on a proper extraction of the vascular tree.
  • Another example is the task of automatically detecting lesions which in many cases resemble the blood vessels. A properly extracted vessel tree may be a valuable tool in disqualifying false positive responses produced by such an algorithm, thus increasing its specificity.
  • the present invention relates to a method for detecting lesions in an image, wherein the detection process includes a wide range of information.
  • the detection process includes a wide range of information.
  • auxiliary information such as
  • the purpose of the method is to provide a framework where this type of covariate information may be included in the detection of individual lesions. This may allow the algorithm to analyse the images like the human grader does, by combining high- level information with low-level characteristics of the image, and may as such introduce a completely new paradigm in the lesion detection algorithm.
  • the method relates to image diagnostics in medicine, such as X-rays, scanning images, photos, magnetic nuclear radiation scanning, CT scannings, as well as other images potentially comprising lesions.
  • the method should be robust in the sense that it should be applicable to a wide variety of images independent of illumination, presence of symptoms of diseases and/or artefacts of the image.
  • Lesions may be any sign of disease or pathological condition that is detectable as local events in the image.
  • Lesions of the retina normally embrace microaneurysms and exudates, which show up on fundus images as generally "dot shaped" (i.e. substantially circular) areas. It is of interest to distinguish between such microaneurysms and exudates, and further to distinguish them from other lesions or pathologies in the image, such as "cotton wool spots" and hemorrhages.
  • the present invention relates to a method for assessing the presence or absence of lesion(s) in a fundus image from an individual, comprising
  • each subset is a candidate lesion area having a probability
  • step d) classifying the candidate lesion area detected in a) with respect to the threshold obtained in step c) as a lesion or not
  • steps a) to d) optionally repeating steps a) to d) until all candidate lesion areas have been classified.
  • the method may include steps by which the lesions detected are corrected with respect to the background, in particular the local background in the vicinity of the lesion, in order to be able to detect lesions independent on the back- ground in the specific image, including variations of background in the images, for example due to varying illumination of the various parts of the image.
  • the invention relates to a system for carrying out the methods according to the invention, such as a system for assessing the presence or absence of lesion(s) in a fundus image of an individual, comprising
  • an algorithm for establishing information from said individual comprising at least one information type selected from the following: clinical information and structural information,
  • step d) an algorithm for classifying the candidate lesion area detected in a) with respect to the threshold obtained in step c) as a lesion or not
  • Said system is capable of incorporating any of the variations of the methods described herein.
  • the invention relates to a method for diagnosing the presence or absence of a disease in an individual from a fundus image of at least one eye of said individual comprising
  • the invention relates to a method for assessing the probability of a diagnosis of diabetic retinopathy in an individual by using the method according to the invention, i.e. a method starting from a fundus image of at least one eye of said individual, and comprising
  • each subset is a candidate lesion area having a probability
  • step d) classifying the candidate lesion area detected in a) with respect to the threshold obtained in step c) as a lesion or not
  • the image may then be classified depending of size and/or numbers and/or placement of lesions in the image, and accordingly, the invention relates to a method for classifying a fundus image comprising
  • Figure 1 Fundus image.
  • Figure 2 Accumulated presentation of lesions detected by expert graders in 199 fundus images.
  • Figure 3 Density presentation of the lesions in Figure 2.
  • Figure 4 Heat density plot of the lesions in Figure 2.
  • Figure 5 3D plot of the density presentation of Figure 3.
  • Figure 6 Designation of probability area in fundus.
  • Figure 7 A flowchart showing the Watershed procedure.
  • Figure 8 1-D example of pixel to process using Watershed algorithm with a toler- ance of 1.
  • Figure 9 The figure shows a partial image of the eye fundus, wherein a circle showing the 75 pixels radius background region of a lesion is arranged.
  • Figure 10 The background region in the gradient image.
  • Figure 11 Flow chart for normalization process.
  • Figure 12 Flow chart for growing process.
  • Figure 13 Schematic drawing showing the calculation of the visibility feature.
  • Figure 14 An example of a grown lesion and the band around it representing the background.
  • Figure 15 An example of overlapping regions.
  • the left panel displays the three regions which are grown from seed points located in the proliferation displayed in the right panel.
  • the band around the largest grown region is used as background for all of the three regions.
  • Figure 16 shows a histogram with the fitted gamma density of true bright lesions in a material of 400 fundus images
  • Figure 16B shows a histogram with the fitted gamma density of false bright lesions in the material of 400 fundus images.
  • Figure 17 shows the fitted visibility distributions of bright lesions, wherein the curve to the left represents false lesions, and the curve to the right represents true lesions.
  • Figure 18 shows a histogram with the fitted gamma density of true dark lesions in a material of 400 fundus images
  • Figure 18B shows a histogram with the fitted gamma density of false dark lesions in the material of 400 fundus images.
  • Figure 19 shows the fitted visibility distributions of dark lesions, wherein the curve to the left represents false lesions, and the curve to the right represents true lesions.
  • Figure 20 3D comparison of the distributions of Example 3 and Example 4.
  • Fovea The term is used in its normal anatomical meaning, i.e. the spot in retina having a great concentration of rods giving rise to the vision. Fovea and the term "macula lutea” are used as synonyms.
  • Image The term image is used to describe a representation of the region to be ex- amined, i.e. the term image includes 1 -dimensional representations, 2-dimensional representations, 3-dimensionals representations as well as n-dimensional representatives. Thus, the term image includes a volume of the region, a matrix of the region as well as an array of information of the region.
  • Lesion in fundus images Any pathology present in the fundus, such as microaneurysms, exudates, hemorrhages, cotton wool spot.
  • lesions refer to the dot-shaped lesions: micro-aneurysms and exudates.
  • Optic nerve head The term is used in its normal anatomical meaning, i.e. the area in the fundus of the eye where the optic nerve enters the retina. Synonyms for the area are, for example, the "blind" spot, the papilla, or the optic disc.
  • Prior probability The term is used in its normal meaning, i.e. the probability before combination with other information. Prior probability is synonymous with a priori probability.
  • Posterior probability The term is used in its normal meaning, i.e. the probability after combination with other information. Posterior probability is synonymous with a pos- teriori probability.
  • Red-green-blue image The term relates to the image having the red channel, the green channel and the blue channel, also called the RBG image.
  • ROI Region of interest.
  • Starting point The term describes a point or area for starting the search for a subset.
  • the term starting point is thus not limited to a mathematical point, such as not limited to a pixel, but merely denotes a localisation for starting search.
  • Visibility The term visibility is used in the normal meaning of the word, i.e. how visible a lesion or a structure of the fundus region is compared to background and other structures/lesions.
  • the images of the present invention may be any sort of images and presentations of the region of interest.
  • Fundus image is a conventional tool for examining retina and may be recorded on any suitable means.
  • the image is presented on a medium selected from dias, paper photos or digital photos.
  • the image may be any other kind of representation, such as a presentation on an array of elements, for example a CCD.
  • the image may be a grey-toned image or a colour image; in a preferred embodiment the image is a colour image.
  • the problem when evaluating the images is to evaluate whether a subset of the image representing a candidate lesion is true or false lesion, or in other words, to determine the probability that the candidate lesion is a true lesion.
  • the present invention relates to assigning a prior probability to each candidate lesion, and then correct the prior probability with other information as described herein, to obtain a posterior probability using the posterior probability to determine whether the candidate lesion is a true lesion or not.
  • the starting point for determining the probability of the candidate lesion to be a true lesion is to determine the visibility of the candidate lesion.
  • the term visibility is used in the normal meaning of the word, i.e. how visible a lesion or a structure of the fundus region is compared to background and other structures/lesions.
  • the visibility of an area may be determined as a vector of features, including intensity, visibility of the candidate lesion compared to the visibility of the vessels, visibility of the edge of the candidate lesion, colour information of the candidate lesion, variance measure of a part of the image and/or a variance measure of the image.
  • the visibility of the edge of the candidate lesion calculated as the orientated candidate lesion area edge gradient, in particular a weighted edge gradient.
  • the visibility feature may be based on a summation of orientation weighted region border gradient pixels.
  • N is the number of pixels in the outline of the lesion.
  • ⁇ be the vector between the center of mass of the lesion and the image point at V - t is then the angle between ⁇ and orientation at the point.
  • the visibility feature may also be calculated from an image wherein the vessels have been "removed”. This is done by subtracting the vessel image (r,c).from the original image I(r,c) , and producing a vessel "restored” image, i.e. an image wherein interpolated background values have been introduced instead of vessels, by
  • the interpolated value may be produced by
  • h(x) exp(-x 2 12) .
  • the kernel width w(r,c) is set to the distance from pixel
  • a potential lesion located at x with visibility v is considered.
  • a single visibility threshold is applied, and A is estimated by
  • T the visibility threshold.
  • the visibility threshold may depend on the type of the lesion, but otherwise the threshold is the same for all lesions.
  • the probability of the candidate lesion is compared to a threshold value, and lesions having a probability above that threshold value are considered true lesions.
  • the idea of the present invention is thus to adapt the threshold to the specific lesion under consideration.
  • the threshold may for example be lowered if several clear lesions are present in the image, as it is then more likely, that the present lesion is also true, or a lower threshold may be used for patients with a long duration of diabetes, as these have a higher prevalence of diabetic retinopathy.
  • the probability of the candidate lesion area may be corrected with the information from said individual, and the corrected probability is compared with a predetermined probability threshold for lesions, or predetermined probability threshold is corrected with the information from said individual, whereaf- ter the probability of the candidate lesion area is compared with the corrected predetermined probability threshold.
  • variable A is a stochastic variable, with a certain distribution, known as the a priori di- stribution.
  • the posterior probability is thus a measure of how likely it is that the lesion is true, obtained by combining prior knowledge summarised by p(x) and the actual obser- vation in the image, summarised by v.
  • the posterior odds are obtained by multiplying the prior odds with the odds determined by the data.
  • the likelihood of the actual observation in the image, for example the visibility of a candidate lesion, to be a true lesion is combined with prior knowledge, in the present context called "information from the individual".
  • Said information may be any relevant information capable of adding information to the actual observation in the image relating to the candidate lesion.
  • said information is selected from the following: clinical information and structural informa- tion. Accordingly, another initial step is establishing information from the individual whose fundus image is studied.
  • ⁇ information may be included in the method, such as at least two different types of information, such as at least one structural type of infor- mation and at least one clinical type of information.
  • clinical information relates in the present context to any kind of clinical and demographic data of the individual under study since they may be believed to be predictors for the progression to (sight-threatening) diabetic retinopathy.
  • the clinical information is one or more clinical information types, for example clinical information types selected from information about age of said individual, information about sex of said individual, information about ethnicity of said individual, informa- tion about diseases of said individual, and information about at least one clinical test of said individual.
  • blood tests may be relevant, such as information about metabolic control variables, such as blood cholesterol level, blood glucose level, and HblAc level.
  • visual tests are of relevance, such as Visual Acuity test or an autorefraction test.
  • the invention may include information about the presence or absence of diseases in said individual.
  • diseases selected from diabetes, atherosclerosis, and hypertension, such as information about duration of disease, severity of disease, or simply a statement that the disease is present or absent.
  • Other information such as information about pregnancy, puberty or previous cataract surgery, may also be relevant.
  • the information or covariates for the individual may be collected in a vector te R d .
  • a common way of modelling the dependence of disease prevalence as function of explanatory variables is by logistic regression, where the following model is assumed,
  • the prior parameters ⁇ R and ⁇ e R d may be estimated in a study where all true lesions are marked in the images, and clinical information is available. Alternatively, previous epidemiological studies in the literature may provide sensible values for the parameters.
  • this prior only depends on variables relating to the patient and thus has the same value for all potential lesions in images from the specific visit. This is included to change the threshold from patient to patient, in order to reflect the fact that, for instance, it is more likely that a patient with a long duration of diabetes has retinal lesions, than a patient with a short duration.
  • structural information includes information of any structures of the fundus image being studied as well as information of any structures in fundus images previously being acquired preferably from said eye. Accordingly, the structural informa- tion may be selected from regional information, lesion information, and vessel information.
  • the structural information includes information of the position of the le- sion with respect to anatomical landmarks, such as the fovea, the optic nerve head and the vessels, information of other lesions in the image, and the information of lesions in other fundus images, for instance images of different fields of the same fundus, or from previous images of the same field.
  • anatomical landmarks such as the fovea, the optic nerve head and the vessels
  • information of other lesions in the image such as the fovea, the optic nerve head and the vessels
  • the information of lesions in other fundus images for instance images of different fields of the same fundus, or from previous images of the same field.
  • the lesion information is selected from information about number of lesions in another fundus of the same individual, information about previous le- sion(s) in the same and/or another fundus of said individual, information of other lesions in the same fundus image, information of a lesion in the same subset of another fundus image of the same fundus, and information about number of candidate lesion areas.
  • the information about previous lesion(s) in the same or another fundus of said individual preferably comprises information about optional previous lesion(s) in substantially the same image subset of the fundus.
  • the knowledge that previously at least one lesion was found in this individual may lead to a lowering of the threshold for the probability.
  • the information about other lesions and/or candidate lesion(s) in the same fundus image preferably comprises information about
  • the method includes information on other lesions in the classification as described in detail below.
  • a slightly more general notation is used for this, so from now on let x 0 denote the (position of) the potential lesion under consideration, and let x ,...,x k denote the neighbouring lesions to x 0 .
  • neighbours should be interpreted in a broad sense, as the neighbouring lesions may be located in other images from the same visit or even in images from another visit.
  • the prior probability of 1 ' s given by p(x 0 ) .
  • the prior pro- bability function p(») may for instance be modelled by any of the spatial priors to be discussed later herein.
  • the regional information may also be or include the information about the region of the fundus image comprising the at least one subset of the image. It has been found that more true lesions are found in some areas of the fundus as compared to other areas of the fundus. In particular the region around fovea, see Figures 2-6, has been shown to exhibit more true lesions than other regions.
  • the anatomical features are normally selected from fovea, optical nerve head and vessels, such as main arcades.
  • the information of the relation of the candidate lesions to one or more of the anatomical features may be included, such as the distance to the anatomical feature or simply information as to whether the candidate lesion is within a certain distance from the anatomical feature.
  • the structural information may include information that is a function of the distance to fovea for correcting the probability in step c).
  • the prior probability may be corrected with the spatial position of the lesion, in order to include the knowledge that lesions are more frequent close to the fovea than in peripheral regions.
  • the prior model is defined by
  • / is the point of the fovea.
  • the prior expresses the finding that it is R times more likely to detect a lesion in the circle of radius ⁇ around fovea, than outside this circle.
  • the radius may for instance be chosen as one disk diameter (DD) to reflect the distinction typically made in grading protocols between the severity of lesions within and outside 1 DD of fovea, see also Figure 6.
  • At least two different types of information are included in the method, more preferably at least three different types of information, such as least four different types of information, five different types of information, six different types of information, seven different types of information, eight different types of information, nine different types of information, or ten different types of information.
  • the method includes the estimation of a subset of the image, wherein the subset may represent a candidate lesion area.
  • the subset may represent a candidate lesion area.
  • the subset is a candidate lesion area.
  • the term subset is used in its normal meaning, i.e. one or more pixels.
  • the subset may be established by any suitable method, for example by filtering, by template matching, by establishing starting points, and from said starting points grow regions and/or by other methods search for candidate areas, and/or combinations thereof.
  • the candidate lesion area(s) are detected by establishing starting points, and from the starting points estimating the subset. Two or more subsets, each representing the same lesion may be detected, such as overlapping subsets or adjacent subsets.
  • the subset is a connected subset, i.e. all the pixels of the subset connects at least one of the other pixels, and it is possible to reach any of the pixels from any of the pixels by following pixels in the subset.
  • the estimation of the subset of the image comprises establishing of the periphery of the subset.
  • the periphery may be established for example by active contour model (snake) (reference "Snakes: Active contour models” by M. Kass, A. Witkin and D. Terzopoulos), by templating or by growing.
  • the subset may be established through establishing starting points.
  • Starting points may be established by a variety of suitable methods and of combinations of such methods.
  • the variability of fundus images is particularly relevant regarding image dynamics; the contrast may vary considerably from image to image and even from region to region in the same fundus image.
  • a proper starting point algorithm should recognize this circumstance and seek to adapt its sensitivity to the image at hand.
  • the image may be filtered and/or blurred before establishing or as a part of establishing starting points for the method. For example the low frequencies of the image may be removed before establishing starting points.
  • the image may be un- sharp filtered, for example by median or mean filtering the image and subtracting the filtered result from the image.
  • the starting points may be established as extrema of the image, such as local extrema.
  • the image is preferably a filtered image, wherein the filtering may be linear and/or non-linear.
  • the extrema may be minima or maxima or both.
  • the filtering method is a template matching method, wherein the template may exhibit any suitable geometry for identifying the lesions.
  • templates are circles, wherein the circles have a radius set as a ratio of the expect- ed diameter of the optic nerve head.
  • the image may be filtered with one or more filters before establishing starting points, or as a part of the step of establishing starting points.
  • starting points are estab- lished by combining two or more filters.
  • the extrema may thus be identified indidually by one or more of several methods, such as the following:
  • the lesions are normally either dark areas or light areas in the image, or at least locally the darkest areas or the lightest areas.
  • a method may be establishing at least one intensity extremum in the image, preferably at least one intensity minimum or at least one intensity maximum. Therefore, in a preferred embodiment at least one local intensity maximum is established.
  • the extrema may be established on any image function, such as wherein the image function is the unsharped image, the red channel image, the green channel image, or any combinations thereof. In a preferred embodiment the image function is the green channel.
  • the method may include establishing at least one variance extremum in the image, preferably establishing at least one variance maximum in the image.
  • the extrema may be established on any image function, such as wherein the image function is the unsharped image, the red channel image, the green channel image, or any combinations thereof. In a preferred embodiment the image function is the green channel.
  • Another method for establishing starting points may be random establishment of starting points, wherein the provocative random establishment is establishing a start- ing point in substantially each pixel of the image.
  • a random establishment may be combined with any of the methods discussed above.
  • the starting points may be established as grid points, such as evenly distributed or unevenly distributed grid points. Again this method may be combined with any of the methods of establishing extrema in the image and/or random establishment.
  • starting points are established by more than one of the methods described in order to increase the propability of assessing the correct localisation of lesions, if present, also with respect to images having less optimal illumination or presenting other forms of less optimal image quality.
  • the starting points are established by localising the local minima and/or maxima of the green channel image function, and let them act as starting points.
  • the subset is established by growing a subset from a starting point.
  • the growing of an object is used to segment an object from the background.
  • the method may be used to grow both dark and bright objects, the algo- rithm for the one simply being an inversion of the algorithm of the other.
  • the most essential part of the growing method is to limit the object with respect to the background. This limitation may be done in any way, for example by examining the visibility feature as described below, for a wide range of isocurves, or object depth, and then simply select the depth, which results in the highest possible visibility feature.
  • the establishment of subsets may be explained as growing q isocurves based on at least one growing feature of the area around the starting point, q being an integer of at least 1 , until the periphery of the candidate lesion area is established. That is for each starting point, a number of isocurves, wherein each isocurve may represent a candidate lesion area, is grown from the starting point.
  • the growing process may give rise to extraction of morn that one subset, the number of subsets for example corresponding to equally distant isocurves.
  • the smallest subset exceeds that of the starting point itself, and the area of the largest subset subceeds a predetermined value.
  • the isocurve being the iso- curve having the highest propability of being a candidate lesion area.
  • the propability may for example be the highest visibility as described below.
  • the subset of the image then implies the region contained by an isocurve resulting from the growing process, and the isocurve itself implies the periphery of the subset.
  • the growing algorithm is initialized in the starting point for the subset. Increasing the height in equidistant levels results in a number of grown isocurves.
  • the step depth may be arbitrarily set, but is normally for practical reasons chosen to 1 , as the pixel levels origins from byte images, which has discrete integer values.
  • the algorithm may continue for the whole image starting from each starting point.
  • it is appropriate to apply at least one limitation to the growing namely that candidate lesion areas above a certain predetermined area are not allowed.
  • another limitation may be applied either additionally or alone, namely that the candidate lesion is limited by a minimum and a maximum number of isocurves.
  • the predetermined value described above is preferably in the range of from 0.1 to 1.0, such as in the range of from 0.2 to 0.8, such as in the range of from 0.3 to 0.6.
  • the watershed algorithm was introduced for the purpose of segmentation by Lantuejoul and Beucher.
  • the idea of watershed is drawn by considering an image as a topographic surface.
  • the image intensity (the gray level) is considered as an altitude with this point of view.
  • a regional minimum is a connected plateau from which it is impossible to reach a point of lower gray level by an always-descending path.
  • As the image surface is immerged some of the flood areas (catchments ba- sins) will tend to merge. When two or more different flood areas are touched, infinitely tall dams (watershed lines) are constructed between them. When finished, the resulting networks of dams define the watershed of the image.
  • the watershed lines partitions the image into nonintersecting patches, called catchments basins. Since each patch contains only one regional minimum, the number of patches is equal to the number of the regional minima in the image. In a preferred embodiment the pixel, with minimum value and which is closest to the center of mass of the region, becomes the origin for the growing algorithm.
  • Steps 1 , 3, 5 and 8 Find minimum unprocessed pixel, and include neighbour pixel with the same value.
  • Borders are touching other regions so enlarge those.
  • the sensitivity of the watershed algorithm may be adjusted by modifying the tolerance level, which makes it possible to except basis with an insignificant depth.
  • the area may be filled, for example by simply performing a flood fill from the starting point to the peri- phery.
  • the subsets may be validated before being corrected with respect to the back- ground.
  • validation is meant that each subset is subjected to a validation step to determine whether the candidate area should classify as a candidate lesion area before assigning a prior probability.
  • the validation is preferably carried out by a feature different from the growing feature(s).
  • the validation step includes calculating the visibility of the candidate lesion area.
  • each subset is a candi- date lesion area having a visibility, and after having assigned visibility features to the candidate lesion area
  • the estimation of the subsets and estimation of the background variation is conducted in one step for each subset.
  • the background variation may be selected from the spatial and/or distributional properties of the original image, or any transformation of this, such as a gradiant image, a curvature image or a Laplace image.
  • the spatial properties may for example be based on a Fourier transformation, cooccurrence matrix, and fractale dimension, and the distributional properties may be moments such as mean, variance skeewness or kurtosis.
  • the lesions may for example be described by a visibility feature as discussed above, which is based on the orientation weighted lesion border gradient observations, and in this embodiment it has been shown to be an advantage to normalize the lesions visibility feature with a mean and standard deviation estimate of the background gradient.
  • the background variation is estimated by sequential identification of out-liers, for example by
  • the upper and lower threshold is determined as a constant multiplied with the standard deviation, for example as the standard deviation multiplied with at least 2, such as at least 3, such as at least 4, such as at least 5 or such as at least 6. It is preferred that at most one pixel is removed in each iteration step c2)
  • the area defined surrounding the candidate lesion area may include or exclude the candidate lesion area itself.
  • the gradient magnitude pixels in step d) include pixels from the candidate lesion area.
  • the area surrounding the candidate area is normally selected to be in the range of from 0.25 to 1.0 of the expected optic nerve head area, such as from 0.5 to 1.0 of the expected optic nerve head area, such as from 0.6 to 1.0 of the expected optic nerve head area. Normally such an area corresponds to a number of pixels in the range of from 100 to 100,000 pixels, such as in the range of from 400 to 64,000 pixels, such as in the range of from 1000 to 50,000 pixels, such as in the range of from 5,000 to 25,000 pixels.
  • the first step of the normalization is to estimate the background gradient of the lesion to correct. This estimation is done by an initial collection of the pixels within a given radius from the lesion center of mass. The amount of pixels is set in accor- dance with resolution of the image assessed. For most purposes the amount of pixels are set in a radius of from 50 to 100 pixels, vide for example Figure 9.
  • Crossing vessels and/or other lesions could influence a gradient estimate of the background, which calls for a robust estimation of the gradient background.
  • the background region in the gradient image is shown in Figure 10, from which the in- cluence of crossing vessels and/or other lesions is clear.
  • an outlier as a value deviating more than two standard deviations from the mean.
  • robust methods may be by filtering the image before collecting the intensities or by using robust estimators, such as the median instead of the mean and the mean absolute deviation instead of the standard deviation.
  • the steps of the methods may be conducted sequentially or in parallel for all sub- sets.
  • Some of the naturally occurring structures of the image may influence the assessment of lesions in a disadvantageous manner.
  • Such structures are for example vessels, and the optic nerve head of a fundus image, since these structures present dark/bright areas in the image. Therefore, some adjustment to the structure is preferred.
  • vascular system may be isolated from the rest of the image content.
  • One method for tracking vessels is a method wherein use is made of the fact that the vessels are linear in a local neighbourhood wherein different filter matrices have different orientations.
  • the localisation and orientation of such line elements may be determined using a template matching approach sometimes referred to as match filters).
  • a preferred method for tracking vessels is by tracking individual vessels from start- ing points representative for vessels, and iteratively grow the vessel network of the retina.
  • a preferred embodiment hereof is described in PCT patent application No. PCT/DK02/00662.
  • the esti- mation of starting points and/or estimation of subsets is adjusted with respect to vessels appearing in the image.
  • the estimation of candidate lesion areas is preceded by detection of vessels in the image.
  • adjustment of starting points means that starting points located in vessels are removed from the plurality of starting points representative for a lesion.
  • subsets of the image having at least a portion of said subset located in a vessel are rejected as a candidate lesion area.
  • Yet another method for adjusting with respect to the vessels is having detected the vessels of the image, the vessels appearing in the image are masked before establishing starting points.
  • the vessels may be masked by any suitable method, for example by masking a number of pixels along the vessel, such as a number in the range of from 1 to 10 pixels.
  • veins and arteries among the blood vessels. This can be important, for example in the diagnosis of venous beading and focal arteriolar narrow- ing.
  • the vascular system observed in the ocular fundus images is by nature a 2-dimensional projection of a 3-dimensional structure. It is quite difficult in principle to distinguish veins from arteries, solely by looking at isolated vessel segments. However, it has been discovered that effective separation can be achieved by making use of the fact that, individually, the artery structure and the vein vessel structures is each a perfect tree, (i. e., there is one unique path along the vessels from the heart to each capillary and back).
  • the artery and vein structures are each surface filling, so that all tissue is either supplied or drained by specific arteries or veins, respectively.
  • a method for distinguishing veins from arteries is described in WO 00/65982, which is based on the realisation that crossings of vessel segments are, for practical pur- poses, always between a vein and an artery (i.e. crossings between arteries and arteries or between veins and veins are, for practical purposes, non-existent).
  • optic nerve head Another structure capable of interfering with the assessment of lesions is the optic nerve head.
  • the optic nerve head As opposed to vessels, the optic nerve head is not necessarily present in all images, this depending on the region acquired by the camera or CCD.
  • the presence or absence of the optic nerve head area is assessed by a robust method before assessing the lesions.
  • Such a method is for example described in PCT patent application No. PCT/DK02/00663.
  • the estimation of starting points and/or estimation of subsets is adjusted with respect to the optic nerve head appearing in the image.
  • the estimation of candidate lesion areas is preceded by detection of the optic nerve head in the image.
  • adjustment of starting points means that starting points located in the optic nerve head are removed from the plurality of starting points representative for a lesion.
  • subsets of the image having at least a portion of said subset located in the optic nerve head are rejected as a candidate lesion area.
  • Yet another method for adjusting with respect to the optic nerve head is, when having detected the optic nerve head of the image, the optic nerve head appearing in the image is masked before establishing starting points.
  • the optic nerve head may be masked by any suitable method, for example by masking a number of pixels around the optic nerve head, such as a number corresponding to a constant multiplied with the diameter of the optic nerve head, optionally of an expected diameter of the optic nerve head, said constant being in the range of from 1.1 to 2.0, preferably about 1.5.
  • the method according to the present includes weighting the visibility in relation to local intensity variation around the lesion in order to reduce false positive lesions due to for example nerve fibre layers, untracked vessels and reflections in the vitreous body.
  • a common feature of these false positives is that the local intensity variation around the lesion is relatively large, contrary to the majority of true lesions, which are located in homogenous areas.
  • the background may be defined slightly differently, in order to avoid that the large lesion is interpreted as background when evaluating the smaller interior lesion.
  • the "foreground” may be the entire con- nected component in the les-image consisting of positive visibility pixels.
  • the background will be defined as above, but relative to this foreground-region. Thus, all lesions that are overlapping will have the same background region. The principle is illustrated in Figure 15.
  • regions grown around fovea and in large lesions may be misclassified as these usually have overlapping grown regions.
  • the local or immediate background may be defined as the band of pixels that are more than B. n and at most B in +B m ⁇ t pixels from the lesion.
  • the distance between a point and the lesion is defined as the smallest distance between the point and a pixel within the lesion.
  • B out is the width of the background-band around the lesion
  • B in is the width of the band separating the lesion and the background.
  • the ratio of the mean green channel intensity in the background and in the lesion may be used to discriminate true and false lesions. For example, a fixed threshold seems most appropriate, * green.out
  • IR thresh preferably is less than 1.1 such as between 1.01 and 1.09, preferably between 1.04 and 1.08 to discriminate a true lesion from a false lesion, a false le- sion having mean intensity ration below IR tresh .
  • ⁇ p . eenfiUt and / mil is the mean of the green channel in the immediate background and in the lesion, respectively.
  • v is the usual normalised visibility measure
  • ⁇ 2 P oi ,m respectively ⁇ 1 poi , out is the variance of the intensities in the poly-smoothed image inside the lesion and in the background, respectively.
  • the variance-weighted visibility measure is then compared with the predetermined visibility threshold as described above.
  • the predetermined visibility threshold it is the corrected visibility that is weighted as described above or the weighted visibility is compared with the corrected threshold.
  • the information regarding the lesion may be used for various purposes.
  • the present invention further relates to a method for diagnosing the presence or absence of a disease in an individual from a fundus image of at least one eye of said individual comprising
  • Diabetic retinopathy is a condition wherein the person has at least one of the following symptoms:
  • IRMA Intraretinal microvascular abnormalities
  • NBD new vessels on disk
  • NVE new vessels elsewhere
  • the invention may be used to quantify the probability of a person having diabetic retinopathy.
  • the presence or absence of true lesions has an impact on the probability of diabetic retinopathy.
  • the present invention also relates to a method for assessing the probability of a diagnosis of diabetic retinopathy in an individual from a fundus image of at least one eye of said individual, comprising
  • each subset is a candidate lesion area having a probability
  • information from said individual comprising at least one information type selected from the following: clinical information and structural information,
  • step d) classifying the candidate lesion area detected in a) with respect to the threshold obtained in step c) as a lesion or not
  • the invention relates to a method for classifying a fundus image comprising
  • the invention further relates to a system for assessing the presence or absence of lesions in a fundus image.
  • the system according to the invention may be any system capable of conducting the method as described above as well as any combinations thereof within the scope of the invention. Accordingly, the system may include algorithms to perform any of the methods described above.
  • a graphical user interface module may operate in conjunction with a display screen of a display monitor.
  • the graphical user interface may be implemented as part of the processing system to receive input data and commands from a conventional keyboard and mouse through an interface and display results on a display monitor.
  • many components of a conventional computer system have not been discussed such as address buffers, memory buffers, and other standard control circuits because these elements are well known in the art and a de- tailed description thereof is not necessary for understanding the present invention.
  • Pre-acquired image data can be fed directly into the processing system through a network interface and stored locally on a mass storage device and/or in a memory. Furthermore, image data may also be supplied over a network, through a portable mass storage medium such as a removable hard disk, optical disks, tape drives, or any other type of data transfer and/or storage devices which are known in the art.
  • a parallel computer platform having multiple processors is also a suitable hardware platform for use with a system according to the present invention.
  • Such a configuration may include, but not be limited to, parallel machines and workstations with multiple processors.
  • the processing system can be a single computer, or several computers can be connected through a communications network to create a logical processing system.
  • the present system allows the grader, that is the person normally grading the im- ages, to identify the lesions more rapidly and securely. Also, the present system allows an automatic detection of lesions and other pathologies of the retina without interference from the vessels, again as an aiding tool for the traditional grader.
  • the present system it is also possible to arrange for recordation of the im- ages at one location and examining them at another location.
  • the images may be recorded by any optician or physician or elsewhere and be transported to the examining specialist, either as photos or the like or on digital media. Accordingly, by use of the present system the need for decentral centers for recording the image, while the maintaining fewer expert graders could be realised.
  • the network may carry data signals including control or image adjustment signals by which the expert examining the images at the examining unit directly controls the image acquisition occurring at the recordation localisation, i.e. the acquisition unit.
  • control or image adjustment signals by which the expert examining the images at the examining unit directly controls the image acquisition occurring at the recordation localisation, i.e. the acquisition unit.
  • command signals as zoom magnification, steering adjustments, and wavelength of field illumination may be selectively varied remotely to achieve desired imaging effect.
  • questionable tissue structures requiring greater magnification or a different perspective for their elucidation may be quickly resolved without ambiguity by varying such con- trol parameters.
  • by switching illumination wavelengths views may be selectively taken to represent different layers of tissue, or to accentuate imaging of the vasculature and blood flow characteristics.
  • control signals may include time varying signals to initiate stimulation with certain wavelengths of light, to initiate im- aging at certain times after stimulation or delivery of dye or drugs, or other such precisely controlled imaging protocols.
  • the digital data signals for these operations may be interfaced to the ophthalmic equipment in a relatively straightforward fashion, provided such equipment already has initiating switches or internal digital circuitry for controlling the particular parameters involved, or is capable of readily adapting electric controls to such control parameters as system focus, illumination and the like.
  • the imaging and ophthalmic treatment in- strumentation in this case will generally include a steering and stabilization system which maintains both instruments in alignment and stabilized on the structures appearing in the field of view.
  • the invention contemplates that the system control further includes image identification and correlation software which allows the ophthalmologist at site to identify particular positions in the retinal field of view, such as pinpointing particular vessels or tissue structures, and the image acquisition computer includes image recognition software which enables it to identify patterns in the video frames and correlate the identified position with each image frame as it is acquired at the acquisition site.
  • the image recognition software may lock onto a pattern of retinal vessels.
  • the invention further contemplates that the images provided by acquisition unit are processed for photogrammetric analysis of tissue features and optionally blood flow characteristics. This may be accomplished as follows. An image acquired at the recordation unit is sent to an examination unit, where it is displayed on the screen. As indicated schematically in the figure, such image may include a network of blood vessels having various diameters and lengths. These vessels include both arterial and venous capillaries constituting the blood supply and return network.
  • the workstation may be equipped with a photogrammetric measurement program which for example may enable the technician to place a cursor on an imaged vessel, and moving the cursor along the vessel while clicking, have the software automatically determine the width of the vessel and the subvessels to which it is connected, as well as the coordinates thereof.
  • a photogrammetric measurement program which for example may enable the technician to place a cursor on an imaged vessel, and moving the cursor along the vessel while clicking, have the software automatically determine the width of the vessel and the subvessels to which it is connected, as well as the coordinates thereof.
  • the software for noting coordinates from the pixel positions and linking displayed features in a record, as well as submodules which determine vessel capacities and the like, are straightforward and readily built up from photogrammetric program techniques.
  • Work station protocols may also be implemented to automatically map the vasculature as described above, or to compare two images taken at historically different times and identify or annotate the changes which have occurred, highlighting for the operator features such as vessel erosion, tissue which has changed colour, or other differences.
  • a user graphical interface allows the specialist to type in diagnostic indications linked to the image, or to a particular feature ap- pearing at a location in the image, so that the image or processed version of it becomes more useful.
  • a very precise and well-annotated medical record may be readily compiled and may be compared to a previously taken view for detailed evidence of changes over a period of time, or may be compared, for example, to immediately preceding angiographic views in order to assess the actual degree of blood flow occurring therein.
  • the measurement entries at examination unit become an annotated image record and are stored in the central library as part of the patient's record.
  • the present invention changes the dynamics of patient access to care, and the efficiency of delivery of ophthalmic expertise in a manner that solves an enormous current health care dilemma, namely, the obstacle to proper universal screening for diabetic retinopathy.
  • the invention further includes a data carrier, such as a CD-ROM, said data carrier including algorithms to perform any of the methods described above, where the data carrier is operably connected to a data system.
  • a data carrier such as a CD-ROM
  • the fitted distributions are based on the 400 images.
  • a first precondition is that the probabilities in the model must sum to one, and therefore q 0 is given by
  • This data material is used in this and the following examples to show models for estimating the lesions.
  • a circle of ⁇ DD-radius centred on the fovea is considered where the probability for a lesion being true is Ratio times greater than outside this circle (see Figure 6).
  • a probability of 0.0021 being a true lesion outside the circle. It corresponds to the number of true lesions divided by the number of seed- points in the 400 images. Then we consider Ratio times this probability inside the circle. Different values of this Ratio have been tested. Results
  • Pr /or Pr obability l + R exp 1 - exp p(type)
  • the first term of this model represents the concentration of true lesions around the fovea.
  • the second one represents the ONH.
  • this prior probability is close to (1+R)p(type) and close to 0 next to the ONH.
  • Example 3 The data material described in Example 3 was used.
  • the EDD is used instead of the DD for all the computations.
  • the results are as good and it is more robust to the ONH-detecti ⁇ n.
  • V ⁇ ior Y ⁇ obability [l + i. FuncFovea(Lesion, Fovea, EDD, ⁇ )] 1 - pitype)
  • NB g4-F means that a gaussian to the power 4 has been used around the fovea.
  • g2-ONH means that a usual gaussian has been used around the ONH in the given model.

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Abstract

La présente invention a trait à un procédé de détermination de la présence ou de l'absence de lésion(s) dans une image et un système pour sa mise en oeuvre, dans lequel ladite image peut être une image comportant éventuellement des lésions, notamment une image en provenance de diagnostique par imagerie médicale, et plus particulièrement une image de fond de l'oeil. Les lésions sont identifiées à partir de points de départ considérés comme zones de lésions candidates. On attribue à chaque zone de lésion candidate une probabilité et ladite probabilité est corrigée en fonction de l'information dérivée du sujet.
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WO2018138564A1 (fr) * 2017-01-27 2018-08-02 Sigtuple Technologies Private Limited Procédé et système de détection de troubles dans des images rétiniennes
CN113425248A (zh) * 2021-06-24 2021-09-24 平安科技（深圳）有限公司 医疗影像评估方法、装置、设备及计算机存储介质
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