EP1002293A1 - Depistage du carcinome mammaire - detection precoce et aide au diagnostic - Google Patents

Depistage du carcinome mammaire - detection precoce et aide au diagnostic

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Publication number
EP1002293A1
EP1002293A1 EP98937356A EP98937356A EP1002293A1 EP 1002293 A1 EP1002293 A1 EP 1002293A1 EP 98937356 A EP98937356 A EP 98937356A EP 98937356 A EP98937356 A EP 98937356A EP 1002293 A1 EP1002293 A1 EP 1002293A1
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EP
European Patent Office
Prior art keywords
fractal
mammograms
data
dimensions
analysis
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP98937356A
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German (de)
English (en)
Other versions
EP1002293A4 (fr
Inventor
Barry Thornton
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Breast Screening Decision Support System R & D Syndicate
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Breast Screening Decision Support System R & D Syndicate
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Application filed by Breast Screening Decision Support System R & D Syndicate filed Critical Breast Screening Decision Support System R & D Syndicate
Publication of EP1002293A1 publication Critical patent/EP1002293A1/fr
Publication of EP1002293A4 publication Critical patent/EP1002293A4/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/502—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of breast, i.e. mammography

Definitions

  • the present invention relates generally to the field of mammography and in particular the invention provides a system and method which is capable of guiding a radiologist to make diagnostic decisions with a higher degree of reliability.
  • FIG 1 a flow chart is shown illustrating the process of screening and re-screening adopted in Australia while Figure 2 shows the screening pathway and the organisational units involved in the screening program in the Australia.
  • the process is voluntary and recruitment levels are low.
  • Australia's Health 1992 only 22 per cent of women aged between 40 and 64 have had a mammogram within the previous three years. Women aged between 45 and 49 years had the highest rate of screening (25 per cent), while those between 60 to 64 years had the lowest rate (17 per cent).
  • Breast cancer is curable particularly when detected at early stages and given proper treatment. Early detection through mammography in almost 50% of cases depends on the presence of characteristic microcalcifications in conjunction with other mammographic readings. (In isolation microcalcifications would account for only about 30% of cancer detection).
  • the typical calcifications seen in breast cancer are clusters of tiny calcium based deposits having thin linear, curvilinear, or branching shapes. However, difficulties exist in interpreting some calcifications when they are tiny and clustered but do not conform to the recognised malignant characteristics s ich as cluster shapes, sizes and spacial distribution.
  • Malignant type tumours do not characteristically have a fibrous outer zone. Zones 4 and 5 are more likely to be different in malignant cases whereas these zones are more likely to be equal in many benign cases and cysts.
  • FIG 4(a) a simple sonogram is illustrated for a patient aged 34, who presented for examination with pain. The sonogram ilhistrates a simple cystic lesion that had been palpated by the clinic surgeons.
  • Figure 4(b) a mammogram for the patient of Figure 4(a) is illustrated. This part of the mammogram shows two simple cystic lesions seen on ectiography with grouped microcalcifications between the cysts (arrowed). Histological examination proved this impalpable calcified area to be intraductal carcinoma with early encepholoid carcinoma.
  • Figure 5 diagrammatically illustrates the locations of microcalcifications in the main histological types of ductal carcinoma: a) In comedocarcinoma the calcifications form at the centre of the involved cut; b) In cribriform carcinoma the psammomatous calcifications form in the cavities of the spongy tumour tissue.
  • a mass usually stellate, but occasionally circumscribed (less than 5%) and often mixed configuration.
  • MRI images show great sensitivity in differentiating between normal tissues and diseased tissue, but is not efficient in detecting early disease in the breast (Australian Institute of Health Report (1990), "Screening Mammography Technology” Better Printing Services, N.S.W.).
  • Magnetic resonance imaging techniques had begun to be used in medicine.
  • MRI presents a hazard-free non-invasive way to generate visual images of thin "slices" of the body by measuring the iclear magnetic moments of ordinary hydrogen nuclei in the water lipids (fats) of the body.
  • MRI By the late 1980s MRI had proved superior to most other imaging techniques in providing images of the brain, heart, liver, kidneys, spleen, pancreas, breast and other organs but, as previously mentioned, is not efficient in detecting early disease in the breast (Australian Institute of Health Report 1990, Screening Mammography Technology).
  • MRI provides relatively high-contrast, variable-toned images that can show tumours already existing, blood-starved tissues and neural plaques resulting from multiple sclerosis. Because it is a very expensive modality and requires long examination times, it is unsuitable as a screening tool. Also, it is not as good in 2D spatial resolution as mammography.
  • Ultrasound has been shown to often augment mammography in situations such as dense breasts and cyst/solid differentiation.
  • Light scanning infra-red transillumination has also yielded iseful diagnostic information when examining some patients with dense glandular breasts b it can also be a controversial modality.
  • the aim of embodiments of the present invention is to provide: (i) observer independent parameter(s) based on fractal analysis of these microcalcification features of calcium hydroxyapatite and weddelite deposits which become even more important when visual fatigue of clinicians occurs in extended reading of mammograms (80 or more mammograms /hour is common), and
  • the present invention provides a method of analysis of a set of mammograms of a current patient to provide an indication of malignancy, including the steps of: a) performing a fractal analysis on the set of mammograms of the current patient to obtain a plurality (n) of fractal dimension values of features in the set of mammograms; b) comparing the fractal dimension values obtained from the mammograms of the current patient, with a database of fractal dimension values for mammograms of patients having a verified diagnosis and predicting the possible presence or absence of malignancies in the current patient by comparing similarities in the sets of fractal values.
  • the different fractal dimensions are measured from a plurality of different views.
  • the different fractal dimensions are calculated from the mammograms taken from two different x-ray views and preferably the different fractal dimensions are measured from mammograms taken from the cranio-caudal (cc) view and the oblique (ob) view.
  • an (n) dimensional plot of (n) fractal values for each previously verified patient diagnosis is created and a critical pair of surfaces is apparent such that almost all coordinates on one side of the pair of surfaces represents a benign condition and almost all coordinates on the other side of the pair of surfaces represent malignant conditions. Cases with coordinates in between the surfaces are indeterminate. There can still be an occasional malignant case in the benign space and vice versa, which would obviously need further separate investigation quite apart from the indeterminate locations.
  • fractal dimensions are of more diagnostic value than others.
  • c) The data extracted from the mammograms is obtained by digitising the mammograms using both a greyscale representation and a black and white representation with a selected threshold for the transition from black to white.
  • the greyscale data and the threshold data are then used as input to a computer program to generate different fractal dimension data. It is also found that improved results can be obtained by selecting the size of the area analysed (ie. how much of the area surrounding the Region of Interest (ROI) is included in the analysis). It has been found that analysis of areas of the mammogram 1 to 1.5 cm square and 4 to 4.5 cm square provide effective results. In the preferred embodiment different sets of fractal dimensions are prepared by analysing two areas corresponding to 1.2 cm and up to 4.2 cm square respectively covering the region of interest on the actual mammogram.
  • ROI Region of Interest
  • the fractal dimensions are calculated for each combination of viewpoint/threshold/area by one of several known methods of fractal analysis.
  • fractal dimensions are used on the digitised images of the ROI without thresholding being affected.
  • it is the Clarke method modified as described later in this specification, using the Caldwell of surface calculation, under the sub-heading "Estimation of 2-3D Type of Fractal Dimension".
  • the Box-Count Method (Voss, R.F. (1986) "Characterization and Measurement of Random Fractals", Physica Scripta, Vol. T13, 27-32) is used. This is required for calculation of fractal dimension of the image of ROI when thresholding is applied to it (ie, binary image, "black and white”).
  • fractal dimension values are determined for each set of mammograms of each patient as set out in the following table. However, as previously mentioned, it has been determined that certain ones of these dimensions appear to be more effective as diagnostic indicators and in some embodiments only these more indicative dimensions are used.
  • the interface region between most of the malignant and benign cases indicated by fractal dimensions is observed by initial inspection on-screen with reference to the database of previously verified cases in which the actual condition of the patient has been verified by pathology.
  • a database of such historical data can be used to indicate a surface in n dimensional space up to 8 dimensions depending upon the particular dimensions selected. For visual indication 3 dimension are used which are the most diagnostic.
  • the data from fractal analysis is combined with other more objective data to enhance the accuracy of the foregoing output indication.
  • This objective data is provided by a radiologist examining each mammogram and grading of features conventionally Lised in respect of several categories.
  • the radiologist uses the particular categories and scales specified in Table 2 below, to grade the microcalcifications in each mammogram or set of mammograms and have the radiologist's initial diagnosis expressed as "overall impression" using the conventional used grading shown in the bottom row of Table 2:-
  • Table 2 Description and rating scales of some qualitative features obsei ed by radiologists.
  • each grading scale provides an additional dimension in the multi-dimensional space of diagnostic indicators
  • the method of the invention is able to provide an indication having greater accviracy.
  • the individvial characteristic gradings b it excluding the "overall impression” grading given by the radiologist are used in conjunction with the fractal data.
  • the radiologist's "overall impression”, being subjective, is excluded from the present method so as to be independent of the radiologist's diagnosis.
  • the vahies of shape and uniformity parameters are subjective on the scale of 1-5, but are not the radiologist's "overall impression” diagnosis.
  • Figure 1 is a flow chart of the pathway for mammographic screening in Australia and assessment and subsequent routine re-screening.
  • Figure 2 is a diagram of the mammographic screening pathway in Australia and organisational units responsible for each screening component
  • Figure 3 is a diagram of a benign cystic lesion with modelled zones (1-5) where zone 1 is the external zone of normal tissue, zone 2 is the microcalcification zone, zone 3 is the fibrous bovindary zone and zones 4 and 5 are the bulk of the cyst;
  • Figure 4(a) is a simple transverse sonogram for a patient aged 34 illustrating a simple cystic lesion; and (b) is a mammogram for the same patient showing two simple cystic lesions and a group (indicated) of microcalcifications between the cysts (Croll, ]., "Ultrasonic Differential Diagnosis of Tumors" (Ed. Kossoff, G., and Fukuda, M., 1984) P.105).
  • FIGS 5 (a) and (b)schematically illustrate two types of ductal carcinomas
  • Figure 6 is a diagram illustrating the two planes of view for the cranio-caudal and oblique mammography views of a suspect location labelled as planes '5' and '4' in the Figure with two other planes X ⁇ and X 2 intersecting nipple location (Lanyi 1988);
  • Figure 7 shows 2 examples of suspicious microcalcification clusters (Lanyi 1988).
  • Figure 8 is a diagram illustrating a method of surface area measurement used in Caldwell's method of fractal dimension measurement
  • Figure 9 is a diagram illustrating a method of surface area measurement Lised in Clarke's method of fractal dimension measurement.
  • Figure 10 graphically illustrates the results of fractal analysis of mammographic data from a first gimip of patients
  • Figure 11 graphically illustrates the results of fractal analysis of mammographic data from a second group of patients
  • Figure 12 graphically illustrates sensitivity and specificity of the model "dist" (distribution of microcalcification).
  • the first procedure is for calculating the fractal dimension of a selected region on the mammogram without applying any thresholding method to the image.
  • the second is the Box Count Method for calculating the fractal dimension of a binary image of calcifications obtained after using a thresholding method on the image.
  • Two areas covering the ROI are selected for the analysis (420 x 420 pixels and 120 xl20 pixels) on each mammogram taken from two views. That is to say four regions are selected. It should be noted that the smaller region of the ROI is inside the larger region. The larger region includes tissue surrounding the actual microcalcifications while the smaller region mainly covers the area where the microcalcifications are located. These four regions are then processed with and without thresholding so that, in total, we acquire 8 image data sets for each individual case and we compute the corresponding 8 fractal dimensions. The eight are labelled as follows: rccc, rice, weec, wicc, rcob, riob, wcob and wiob.
  • the first character “r” denotes the region of 120 x 120 pixels while “w” means the whole larger area of 420 x 420 pixels.
  • the second character “i” or “c” corresponds to the image without or with thresholding to extract the microcalcifications respectively.
  • the last two characters represent the cc or ob views.
  • thresholding techniques are needed to apply to the digitised mammograms before we can estimate the fractal dimension of the extracted calcifications.
  • a threshold technique is described in Section 2.
  • the fractal dimension of the extracted calcifications are between 1 to 2.
  • the box-count method is employed to estimate the fractal dimension. It is described by Voss (1986) that
  • N(u k ) is the count of boxes that cover the extracted calcification at the scale of u k .
  • Lundahl et al. (1985) developed a method for determining the fractal dimension of digital coronary angiograms. Caldwell et al. (1990) and Byng et al. (1996) used that method (a similar version) to determine the fractal dimension of digitised mammograms (region or sub-region of the image of the breast alone). Bartlett (1991) has investigated the method with a minor modification of vising weighted least squares regression and has commented aboLit the method that it may be used with confidence only if the range of dimensions of interest is small.
  • the digitised image is vis ialised as a three dimensional image, having columns of different heights.
  • Fig. 8 shows the top area and two "exposed" sides of a particLilar rectangvilar column.
  • the functional relationship of A( ⁇ k ) and ⁇ k is
  • Caldwell et al. (1990) are that the former has taken the unit length u as 1 and has not stated precisely how they calcLilate the height of the rectangular column at different ⁇ k .
  • the matrix has the size of (n + 1) x ⁇ n + l) and the indexes i and ; in the equation of A( ⁇ k ) for the side area start from 1 to n k .
  • n for calculating the side area, it means that side areas of the two outer boundaries are included. It seems not too appropriate as it introduces some edging effect.
  • the fractal dimension then is calc ilated as 2 - fa, where b is the slope of the line from the log-log regression of surface area A ⁇ k ) vs the area ⁇ of the square.
  • the surface area calculations are definitely different between
  • Caldwell et al. and Clarke. More importantly, their underlying models are different. The one from Caldwell et al. is related to Richardson's Law (Mandelbrot, 1977), ie.,
  • M( ⁇ ) is the measured property of a fractal and the respective e d and f d are the Euclidean dimension and the fractal dimension.
  • e d 2. It seems to us that Richardson's Law applies to exact self-similarity.
  • the model of Clarke is chosen but instead of calculating the surface area using the triangular prism, the rectangular column approach suggested by Caldwell et al. (1990) is adopted because of the computational efficiency.
  • the Box Count method as vised for the 1-2D calculations can detect differences in cluster shape and distribution because it involves a number of grid "boxes" at different scales used to cover the region of calcification and counts are made of calcifications in these different sized grid boxes.
  • the modified Caldwell/Clarke method used for 2-3D calculations relies only on the total svirface area at different scales in a 3D representation which is provided by the imaged viewed without any thresholding, the representative "height" of columns on the pixels above the 2D plane depending on the opacity and intensity of the pixels in the digitised image of the various calcifications. Because the method relies on total surface area of a cluster, it is sensitive to changes in shape and size of a cluster and to differences in uniformity between different clusters. 2. Threshold Method
  • Thresholding is a well-known technique for image segmentation. It tries to extract objects from their background. The method of Otsu (1979) has been employed. It is a global, point-dependent techniqvie. It is global thresholding because it thresholds the entire image with a single threshold value. If the threshold value is determined solely from the gray level of each pixel (without considering the local property in the neighbourhood of each pixel), the threshold method is called point dependent (instead of region- dependent).
  • the Otsu method is based on discriminant analysis. It maximises the class separability.
  • the recommended discriminant criterion fvmction (or measures of class separability) is where are are the between-class variance and the total variance of levels, respectively. Since is independent of f (a gray level value belong to a set of gray level for a given image), maximisation of ⁇ with respect to t is equivalent to maximise .
  • t* Once the optimal threshold value t* is determined, a binary image can be obtained.
  • Figure 10 shows the plot of 211 cases examined to date prior to processing the Western Sydney data. The new test results (Western Sydney) are then plotted separately in Figvire 11.
  • the decision we are required to make is to choose, for a given patient, one of K possible diagnoses; in other words, the patient is svipposed to be in one of instates, called states of nahire ⁇ k . Assigning a state ⁇ k to a patient is called action a k .
  • Factors which intervene in the decision making process are: • Sample information, provided by statistical investigation of experimental data.
  • Bayes formula for posterior probability PP k (/) of each state of nature for a patient/ is as follows:
  • the optimal decision corresponds to the diagnosis of lowest expected loss.
  • Multiple Logistic Regression Multiple logistic regression is used to predict a dichotomous outcome variable from one or more predictor variables which may be measured on any scale (categorical, ordinal or interval).
  • the procedure has the option of stepwise selection of predictors to build a model Linder a user-specified significance level to include a variable and a different significance level to remove a variable from the equation.
  • the user also needs to select a Cutoff Probability Value for the Classification Table between O and 1 (the recommended value for overall optimum classification is 0.5). See Table 3 for the details of the description of the values a, b, c, d vised in the conventional table shown there.
  • the formula used in the logistic model is
  • ⁇ is a stratum-specific constant and ⁇ j . . . ⁇ k are the respective coefficients of the predictors x t . . . x k .
  • Table 3 is used to represent how many true positives, true negatives, false positives and false negatives occurred after we had acquired the pathology results for the cases studied. The corresponding sensitivity, specificity and overall correct classifica tion rates are calculated as follows: Predicted
  • Artificial Neural Networks are empirical models that approximate the way it is thought neurons act in the hviman brain. It is used to mimic some of the brain's capabilities.
  • the ANN classifier can be thought of as a black box: patient data is input and a classification is provided as outpvit. In between there is a network process which converts the input data to the output class. This is meant to be rovighly analogous to what happens in the brain whereby an inpvit pattern is converted into a perceptron via neural networks.
  • Model A the resvilts for a model (Model A) for the "Wesley & Netherlands" data using only wccc, wiob, wcob as used in Fig 10, the sensitivity, specificity and the correct classification were 68%, 82% and 76% respectively.
  • the same model with the Western Sydney data (as in Fig. 11) has the corresponding performance measures as 67%, 48% and 54%.
  • a much better model (Model B) is to use all 8 of the fractal dimensions and all of the qualitative predictors (except the "Overall impression” predictor by the radiologist which is excluded so as to enable a comparison to be made between the model and the human expert performance) .
  • TP, FN, TN and FP denote True Positive, False Negative, True Negative and False Positive respectively.
  • the dark region of the top bar indicates the proportion of subjects that is positive.
  • the lower bar consists of segments representing the proportions of individuals with TP, FN, TN and FP test outcome.
  • the "double-bar diagram” is a visvial representation of the analysis of test performance. It has the practical advantage of being easily drawn by most standard graphics software packages (Brenner, H. (1994). "Visual Presentation of Analysis of Test Performance", Journal of Clinical Epidemiology, 46, 1151-1158.)
  • Table 4 The performance of models A and B established for Diagnostica's Bayesian Method. Model C is obtained by comparison of radiologist's "Overall Impression" with the true outcome.
  • Table 5 shows the time required for each component of the entire analysis for a single case in isolation.
  • Table 5 The estimated time components required to process a new individual suspicious (needing careful observation) case in isolation. (In practice, scanning would be off-line and image manipulation time could be shortened.)
  • the images from the Wolfson Image Analysis Unit are cases of stellate distortion and do not have immediate use in our microcalcification project.
  • the occurrence of microcalcifications is in only about 30-40% of mammograms and of these only a small percentage turn out to be malignant so it was necessary for vis to be provided with additional malignant examples in order to test the ability of our methods to distingviish malignant from benign. Therefore, the statistical distribution from each source is different in these tests.
  • qualitative parameters from different centres were obtained by different observers. Therefore for our method, the observers were required to follow the same set of guide lines, ie., vise the same set of parameters and same grading levels as in Table 1 when they assessed the mammograms. Perhaps, in current practice different observers may use exactly the same set of parameters or may give different weights to the parameters in their assessment.

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  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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  • Physics & Mathematics (AREA)
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Abstract

L'invention a trait à une méthode d'analyse d'un jeu de clichés mammaires d'une patiente en cours d'examen visant à fournir des indications relatives à la malignité. Cette méthode repose sur les opérations suivantes: (a), exécution d'une analyse fractale du jeu de clichés mammaires de ladite patiente afin d'obtenir plusieurs (n) valeurs de dimensions fractales d'éléments révélateurs dans le jeu de clichés mammaires, (b), comparaison des valeurs de dimensions fractales tirées des clichés mammaires au contenu d'une base de données de valeurs de dimensions fractales de patientes à diagnostic vérifié, la comparaison des similitudes existant dans les jeux de valeurs fractales permettant de prévoir la présence ou l'absence possibles de malignités chez la patiente en cours d'examen.
EP98937356A 1997-08-08 1998-08-07 Depistage du carcinome mammaire - detection precoce et aide au diagnostic Withdrawn EP1002293A4 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
AUPO8481A AUPO848197A0 (en) 1997-08-08 1997-08-08 Breast screening - early detection and aid to diagnosis
AUPO848197 1997-08-08
PCT/AU1998/000621 WO1999008225A1 (fr) 1997-08-08 1998-08-07 Depistage du carcinome mammaire - detection precoce et aide au diagnostic

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WO2001043067A2 (fr) * 1999-12-10 2001-06-14 Durand Technology Limited Ameliorations relatives a des applications des techniques fractales et/ou chaotiques
EP1252588B1 (fr) * 2000-01-21 2007-06-27 Health Discovery Corporation Procede de manipulation, stockage, modelisation, visualisation et quantification d'ensembles de donnees
US9008382B2 (en) 2009-08-03 2015-04-14 Ralph Highnam Method and system for analysing tissue from images

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