Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
  • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
  • G06V10/754—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries involving a deformation of the sample pattern or of the reference pattern; Elastic matching
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/28—Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
  • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
  • G06V10/76—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries based on eigen-space representations, e.g. from pose or different illumination conditions; Shape manifolds

Definitions

• the present invention relates to a method and apparatus for classifying structural anomalies by characterizing the shape changes via their bounding surfaces and comparing the shape changes with shape measures representing known populations.
• Detection of subtle changes in shape of structures can often times be of critical importance for detecting anomalies that are potential indicators of defect or disease.
• Minor shape changes of structures can often have major catastrophic results. For example, deformation in building construction components may lead to a significant decrease in structural integrity of the entire construction. Also, for example, minor deformations in high resolution, complex machined parts may lead to incompatibility with other machined parts and ultimately to the degradation of the machined device's performance. Additionally, alterations of the shape of human anatomical organs may, for example, be an early warning sign of disease such as cancer. In each of these case, early detection of shape anomalies may be of critical importance.
• diagnosis of many diseases requires painful, invasive testing. For example, a biopsy of suspected diseased tissue is often required to determine if a disease exists within the human body. While non-invasive imaging techniques such as MRI, CT and Ultrasound exist and provide valuable information regarding injury, such techniques provide little to no assistance in directly quantifying structural changes used in the diagnosis of disease.
• Conventional methods for characterizing human brain disease involves no automation for characterizing shape change associated with bounded volumes and their connected surfaces. The most common approach to defining human brain disease is the comparison of the total volumes of selected brain substructures. Volume determinations are usually made using manual techniques of outlining or counting stereological points around and within the selected volume.
• Shape characterization is a method which allows a user to determine abnormalities and changes in shape of structures. Such shape characterization has a multitude of practical applications. For example, shape characterization techniques may provide a means to detect shape changes in human anatomical organs. Also, for example, shape characterization may provide a means to achieve quality control in automated inspections of structural elements.
• Shape characterization of human anatomical organs may provide a non-invasive manner in diagnosing abnormal growths, diseases and potential health risks. Cancerous organs with abnormal shapes might be quickly identified without the need for invasive diagnostic techniques. Automated shape characterization of the brain can detect subtle changes in brain volume not presently possible by conventional methods. Gaussian Random Fields on Sub-Manifolds for Characterizing Brain Surfaces, Sarang C. Joshi et al., incorporated herein by reference, discloses a method of using computer hardware and computer software for automated characterization of human brain substructures and the associated disease. Specific diseases have been studied and the alterations of shape on human organs have been observed and classified. By image comparison with known classified diseased organ shapes, early, non-invasive, detection of diseases may be achieved.
• One disadvantage of conventional methods is the inability to detect the critical subtle changes in human organs such as the brain.
• Another disadvantage of conventional diagnostic methods of human organs is that they require invasive techniques.
• early detection and non-invasive methods are goals of disease diagnosis, it would be desirable to provide a means and method for disease diagnosis through the use of shape characterization.
• shape characterization While the preferred embodiment of this invention is in the characterization of human anatomy, it should be noted that the present invention is not limited to biological application.
• characterizing shapes of non-biological structures such as automated inspection of parts for quality control, for example, may be accomplished.
• the present invention overcomes the above-described disadvantages, and provides a method and apparatus for automated shape characterization.
• the present invention also provides a method and apparatus for automated shape characterization that allows for the detection of disease by means of non-invasive imaging techniques. Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims.
• a method and apparatus consistent with the present invention automatically classifies shapes, for example, automatically classifying populations via shape characterizations of sub-manifolds of points, curves, surfaces and sub-volumes. Where a population is a group of individuals exhibiting a common characteristic, also referred to as a class.
• a method consistent with the present invention includes the steps of: 1) acquiring images of structures using conventional techniques and from the acquired image creating a complete map based on a transform function; 2) creating a database of known shape and volume changes associated with known classification, e.g.
• a method consistent with the present invention includes creating a database of known normal and diseased states of a particular structure, comparing the image of a structure under a study with the images of the database and using a probability distribution function to determine whether the image under study is closer to a known anomalous image or to a normal state image.
• the method makes this determination based on the results of the probability distribution function which calculates the difference between a target and a template image and the probability of a match.
• FIG. 1 depicts the template image with a substructure and its bounding surface identified in the template and mapped on to a target image
• FIG. 2 depicts the mapping of the template sub-volume associated with a substructure of interest on to a family of target sub- volumes
• FIG. 3 depicts the mapping of the template bounding surface associated with a substructure of interest onto a family of target bounding surfaces
• FIG. 4 depicts four surface harmonics computed using the commercially available Automatic Dynamic Incremental Non-Linear Analysis (ADLNA) software package associated with the surface of the hippocampus of a brain for expanding vector fields;
• ADLNA Automatic Dynamic Incremental Non-Linear Analysis
• FIG. 5 depicts a plot of the mean and standard deviation as error bars obtained from maps for normal brains and brains of patients suffering from schizophrenia for the first 25 surface harmonics;
• FIG. 6 is a flow chart representing the method of automatic shape characterization
• FIG. 7 is a flow chart representing the method of automatic shape characterization for use in diagnosing human brain disease.
• a method and device are provided to detect and classify anomalies via shape characterizations of sub-manifolds of points, curves, surfaces or sub-volumes.
• a method and apparatus automatically characterizes human brain substructures and diseases associated with neuromo hmetric abnormalities in these brain substructures.
• the method characterizes the shape changes of brain substructures via their bounding surfaces using a series expansion of the vector fields associated with the volume and shape of the bounding surface of the substructures selected for analysis as it deviates from other individuals in populations.
• a database of known normal and disease state images is created, by an expert in the field or by known techniques in the art, of the subject under study.
• a doctor studying brain disease uses clinical imaging techniques such as, for example, CT, MRI, and Ultrasound to acquire an image of a patient's brain.
• CT computed tomography
• MRI magnetic resonance imaging
• Ultrasound to acquire an image of a patient's brain.
• the doctor or operator enters additional information associated with the image into the database.
• This additional knowledge includes information regarding a diagnosis of a particular disease that afflicts the patient's brain.
• the additional information helps to classify the image when organized in the database.
• the acquired image is then subjected to the automated volume matching transform function described in U.S. Patent Application No. 08/678,628, incorporated herein by reference.
• An automatic volume matching transform function automatically generates a complete mapping of one domain to another, expressed symbolically as h : ⁇ « ⁇ on the entire volume of the acquired image.
• the transform function generates a map of the region ⁇ of the image.
• the transform function used to generate a map of the region may be any of the mapping transform functions described in U.S. Patent Application No. 08/678,628, for example, the transform function described in Equations (6) and (7) of U.S. Patent Application No. 08/678,628, as well as any other transforms known in the art to generate a target map.
• the target map being a generated map of the transformed new image.
• the new image being the image of a structure presently being examined or under study.
• these maps are restricted to preselected embedded submanifolds, and automatically carry the template anatomical information onto the target anatomy.
• This embodiment provides an automated method for understanding the neuroanatomy of the target image.
• the template image being a map stored in the database formed by the transformed mapping of previously examined images.
• These maps can be extended as well to include the mapping technology disclosed in U.S. Patent Application No. 08/678,628 and associated with tumors and other morphometry changing normal and or disease states.
• the various acquired images are entered into the database and classified into different classes (populations) representing various states of the brain. These states include normal brain states, and different disease brain states.
• Each different disease brain states include images of various brains suffering from brain cancer, Alzheimer's disease, Parkinson's disease, schizophrenia, and attention deficit and learning disorders of childhood.
• the normal brain state includes images of various brains in a normal state.
• the collection of images and their respective transformed maps associated with each population are arranged to represent the various substructures of each of the various populations.
• a database is created of image maps which correspond to either normal or one of a disease state.
• the template image of the image previously stored in the population is transformed with the map of the new image under study.
• a statistical measure such as for example, a mean composite sub-volume or bounding surface is generated which represents the average shape characteristic of each population.
• the characteristic average image is refined as new images are classified into the population.
• the populations are characterized and distinguished from one another using probability distribution function measures corresponding to the different populations. For example: Population 0 : P 0 , Population , : P, , . . ., Population N : P N
• P 0 , P 1 , . . ., P N represents the probability distribution function for each defined populations.
• the probability distribution functions are constructed for each population from a training set of individuals belonging to the population using a preselected inclusion and exclusion criteria known to the doctor or operator.
• An embodiment of the present invention is suited for the study of normal and disease populations of schizophrenics.
• a database has previously been established with a population of images representing normal brain states and a population of images representing diseased brains suffering from schizophrenia.
• the probability distribution functions for this example are constructed from a population of individuals defined as normal using preselected clinical inclusion and exclusion criteria.
• the disease shape change measure is constructed from populations of individuals diagnosed with the disease.
• M(j) is a manifold of dimensiony-1, 2, 3
• x is a point on the manifold M(j)
• ⁇ k (x) is a 7R 3 valued basis function
• u(x) is the transforming vector field expanded in the orthonormal basis functions ⁇ k (x).
• a template image known to be associated with an image, a target subvolume of interest and a bounding surface, I 0 , M 0 (3), M 0 (2), respectively is previously stored in a database.
• the first step is to construct biologically meaningful subvolume M 0 (3) and associated surface M 0 (2) in starting volume ⁇ with associated image I 0 that defines the substructure of interest.
• a new target image, representing a new unknown brain structure, is acquired using clinical examination techniques such as CT, MRI, or
• the template image is then mapped onto the target images to acquire a map of the substructure in interest I NEW .
• the map of the new image is accomplished using the automated volume matching transform functions described above and in U.S. Patent Application No. 08/678,628.
• the automatic volume matching transform functions automatically generate a complete map h : ⁇ « ⁇ on the entire volume of the acquired image. These transform are invertible.
• the map of the new image can be formed by mapping the template image onto the new image (target) or vice versa.
• a population characterizing the disease state suffering from schizophrenia, made up of N anatomies are measured via their anatomical imagery /,, I 2 , ..., I N .
• Each mapped image /,, I 2 , ..., I N . is also generated using the same transform function and template image to acquire a map of the substructure in interest.
• FIG. 1 shows ⁇ , a background coordinate system for the template image (mean composite image) 100; a template brain 101; a surface of the template brain 102; an identified substructure of the template brain 103; and a bounding surface of the sub-structure of interest in the template brain 104.
• FIG. 1 also depicts: a background coordinate system of the new image 105; a map of the target image (new image) 106; a surface of the target image 107; a sub-structure of interest of the target image 108; and a bounding surface of the target image 109.
• a set of maps is generated using transforms 111 and 112 to map template image 104 onto target image 109.
• the new image transform map I New can be compared to known image maps to determine if the new image map is properly classified within a particular population.
• the following changes in volume, shape and symmetry allow the doctor to most accurately detect necessary changes in the shape of the structure.
• polar decomposition To study scale and size parameters, use polar decomposition:
• A SO , S e symmetric matrices, O e S ⁇ (3) .
• S is a 3x3 symmetric matrix
• O is a 3x3 orthogonal matrix with determinant -1
• SO(3) is the Special Orthogonal group of dimension 3. Elements of the symmetric group have 6 entries encoding scale; SO(3) the special orthogonal group.
• A SO , S e symmetric matrices, O e 0(3) , (5) where O(3) is the Orthogonal group of dimension 3.
• the variation in the new image must be compared with the probability distribution functions defining each population. If the new image maps falls with the probability distribution functions for a particular population, the new image can be said to be classified as one of that population. The comparison with the probability distribution functions will discussed below. B. Generating the average shape characterizing each population.
• each image classified as a member of a particular population adds to generate an average shape characteristic of the population.
• the average shape includes a mean composite sub-volume and a mean composite bounding surface.
• sets of maps are generated which represents the various substructures.
• To define each population a template image is created that represents the average shape. These are calculated using probability measures on the vector fields restricted to the sub-volumes and associated surfaces.
• FIG. 2 depicts the calculation of a mean composite sub-volume and a mean composite bounding surface characteristic of the population respectively.
• a new sub-structure of interest 200 is transformed by the template images of the substructures of interest existing as members of the population 204-206.
• the template images 204-206 are mapped onto the new image of the sub- structure of interest using equation 8. This is depicted in FIG 2 by 201-203.
• the resulting map is equivalent to the mapping of the mean composite sub-volume 207, using the transform function onto the template image 200 to form a new mean composite sub- volume of the average shape characteristic of the population.
• the mean composite sub-volume of the average shape is refined to reflect the addition of the new member of the population.
• the mean composite bounding surface of the average shape is refined.
• a new sub-structure of interest 300 is transformed by the template images of the sub-structures of interest existing as members of the population 304-306.
• M construct the average scale: K mp 0, 1, 2, 3. (9)
• S' is the scale transformation associated with the i' h anatomical imagery in the kth population.
• the template images 304-306 are mapped onto the new image of the substructure of interest using equation 9. This is depicted in FIG. 3 by 301-303.
• the resulting map is equivalent to the mapping of the mean composite bounding surface 307, using the transform function onto the template image 300 to form a new mean composite bounding surface of the average shape characteristic of the population.
• the mean composite bounding surface of the average shape is refined to reflect the addition of the new member of the population.
• an embodiment of the present invention creates average shapes characteristic of the shapes of the sub-structures which are classified as members of a particular population.
• any new image which matches the average shape of a particular population can be said to be a member of that population.
• the average shape of the sub-structure of interest which characterizes the population suffering from schizophrenia will differ in shape from the average shape of the sub-structure in interest which characterizes the population representing a normal state.
• new images are acquired, they are mapped and compared to the template images of the average shape of each population. If a match is found to either average shape, the new image is classified as a member of that population.
• a disease may be classified based on its shape alone.
• the hypothesis testing approach allows the present invention to better determine whether a new image under study falls within the probability of being classified with one population over another even though the shape of the new image's structure varies from the average shape characterizing each population.
• a probability distribution function is generated, by estimating the mean and covariance through Gaussian processes, from the family of maps making up each population. These maps are characterized as Gaussian processes indexed over the scale and size parameters which parameterize and define the vector fields. These Gaussian processes are represented by their mean and covariance operators.
• scale groups For the scale and size parameters (collectively known as scale groups), the means and covariances of the scale groups are empirically estimated.
• the scale group as a 6 x 1 vector, thus the mean S is a 6 x 1 vector, and the covariance ⁇ s is a 6 x 6 matrix.
• ⁇ k(x) is a 7R 3 valued function
• U k is the mean weight associated with the k th basis function
• U(x) is the displacement vector field expanded in the orthonormal basis function ⁇ k ( ⁇ );
• Uk is the mean weight associated with the k" 1 basis function
• U(x) is mean displacement vector field
• K(x, y) is the covariance operator of the displacement vector field
• o lk is the covariance of the i' h and the k"' weights.
• h k S k O k + b k generating the set of scale matrices S* containing 6 parameters.
• the scale group as a 6 x 1 vector.
• the estimates for the mean and variances of the populations S k , ⁇ k are given by
• S k is the scale transformation associated with the i' h anatomical imagery in the k" 1 population
• S * is the mean scale transformation for the k" 1 population
• ⁇ is the estimate of the scale covariance.
• the mean S is a 6 x 1 vector
• the scale covariance ⁇ s is a 6 x 6 matrix.
• the mean and covariance for the vector fields is estimated from the family of maps SET - U.
• Given are a set of anatomical maps, normal and disease SET - U (u ...,u ⁇ ) from a Gaussian random field with mean-field and covariance (U, K ⁇ ).
• the maximum-likelihood estimates for the mean and variances of the two populations normal, disease are given by
• V" rV basis function of the normal population and 2 ⁇ u * s me covariance of associated with the normal population.
• FIG. 4 shows samples from a complete orthonormal basis ⁇ k ⁇ for expanding vector fields on the surface, in this case the hippocampus.
• the complete orthonormal basis of the surface are constructed numerically using finite elements codes, such as ADLNA for example, or empirical eigenfunction methods.
• the complete orthonormal base can be generated for any smooth geometry.
• Four harmonics of the orthonormal base specific to the hippocampus geometry are illustrated in FIG. 4. These are depicted as deviations around the template geometry.
• the panels show the graph of points generated by ⁇ x + ⁇ k (x), x e M (2) ⁇ , and ⁇ * the k - th harmonic.
• FIG. 4 various deviations from the mean composite image that still fall within the image probability distribution function of a particular population.
• the left panel of FIG. 5 shows the mean template constructed from ten brains hoM 0 (2). Shown in the middle and right panels of FIG. 5 are the maximum-likelihood estimates of the means and variances ( U nieo 2 ) of the first 25 surface harmonics representing maps from the provisory template. The middle panel shows the means U of the first 25 surface harmonics representing deviation of the population of four anatomies from the provisory template. The right panel shows the variances, ⁇ 2 .
• an embodiment of the present invention can automatically diagnosis a disease state based on changes in the structure shape.
• D. Automated Diagnosis of Disease State Via Deviation Morphometric Change In order to automatically diagnose a disease state based on shape characterization, the template image is transformed onto the mapped new image (target image) to determine the deviation of the mapped new image from the average shape which characterizes each population. Once the mapped new image is determined to fall within the variation limits of a particular average shape, characterizing a population, the probability distribution functions that have been established determine whether the new image can be classified as a member of that population. Each population is represented by a particular shape of the structure characterizing that population which in turn represents a particular disease or normal state.
• I represents the map of the new image
• P represents the map of the new image
• the constant represent prior knowledge of the probability that the new image is of a particular population.
• a method consistent with the present invention calculates representations of normal and disease states associated with the composite templates and sample mean and variance statistics for both normal and disease separately generated from population statistics characterizing these states,
• h ⁇ is the mean transformation associated with the normal population
• h is the transformation associated with the Disease population.
• the mapped template image to image of the patient's brain is calculated according to :
• FIGs. 6 and 7 depict a flow diagram representing the method described above.
• FIG. 6, 600 shows a pre-generated database containing characteristic image map models and their corresponding probability distribution function. Each image map and corresponding probability function characterizing a particular population.
• the examination of a new image and structure of interest using conventional imaging techniques is shown at 601.
• 602 shows the generation of a map by applying the mapping transform to the image.
• the map is then restricted to study a substructure of interest 603.
• a shape measure is then computed at 604 for comparison with known population shape measures stored in the database. This step is repeated for each image to be studied 605.
• the map of the new image is compared with each image map characteristic of each population 606.
• a classification into an existing population is determined based whether the new image's map falls within the probability distribution function of a particular population 607.
• the mean composite average shape map of that population is recalculated to account for the new shapes's variation 608.
• This recalculated average shape allows the model of each class to be built and refined.
• Both the mean composite subvolume and the mean composite bounding surface are recalculated for the population. After the mean composite subvolume and the mean composite bounding surface are recalculated, the process is restarted for a new image at 600.
• FIG. 7 depicts the simplified example of classifying various brain images into either a normal or schizophrenic population.
• a database is pre-stored with image maps of normal and schizophrenic brain images 700. Average shapes including mean composite subvolumes and mean composite bounding surfaces are calculated for each population 701 and stored in the database. The probability distribution function is also calculated 702.
• An image of a new brain to be studied is acquired using clinical examination techniques 703. These techniques include MRI, CT, and Ultrasound.
• a map of the new image is generated by mapping a template image stored in the database onto the new target image using a transform function 704. The new image map is restricted to a specific substructure of interest 705. In this example, the hippocampus of the brain is selected.
• a shape measure is then computed at 706 for comparison with known population shape measures stored in the database.
• the new image map is compared to the map of the average shape for either population 707.
• a classification into one of the populations is made based on whether the map of the new image falls within the probability distribution function of either population 708.
• the average shape of that population is recalculated to account for the variations in shape the new image has from the existing average shape 709. Steps 703-709 are repeated for each new brain image to be studied.
• a model including an average shape and probability distribution function of each class is recalculated to account for the variation in shape of each new image classified into a class 710.
• a machined component of a complex mechanical device may be produced with certain acceptable tolerances.
• the machined component may function properly within the entire devices, provided the component is machined within acceptable tolerances.
• An embodiment consistent with the present invention might classify machined components as normal (i.e. within acceptable tolerances), diseased (i.e. defective).
• Additional classifications may be arranged for diseased components which match shape characteristics for other components which may, for example, be subjected to further machining into other functional components, or into a classification of a completely defective component which must not be used for any reasons an so on. Imaging machined components automatically as they are completed off the assembly line may provide an efficient quality control.
• defective parts may be categorized into classification which may be acceptable for recycling into other components. Defective parts with no functional value may be correctly classified and removed from the assembly line. Thus, complex devices may be efficiently manufactured and quality control maintained.

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