kmed: a knowledge-based multimedia medical database system
DESCRIPTION
KMeD: A Knowledge-Based Multimedia Medical Database System. Wesley W. Chu Computer Science Department University of California, Los Angeles http://www.cobase.cs.ucla.edu. KMeD. A Knowledge-Based Multimedia Medical Distributed Database System - PowerPoint PPT PresentationTRANSCRIPT
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KMeD: A Knowledge-Based KMeD: A Knowledge-Based Multimedia Medical Database SystemMultimedia Medical Database System
Wesley W. ChuWesley W. ChuComputer Science DepartmentComputer Science Department
University of California, Los AngelesUniversity of California, Los Angeles
http://www.cobase.cs.ucla.eduhttp://www.cobase.cs.ucla.edu
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KMeDA Knowledge-Based Multimedia Medical Distributed Database SystemA Cooperative, Spatial, Evolutionary Medical Database SystemKnowledge-Based Image Retrieval with Spatial and Temporal Constructs
Wesley W. ChuWesley W. Chu Computer Science DepartmentComputer Science DepartmentAlfonso F. CardenasAlfonso F. Cardenas Computer Science DepartmentComputer Science DepartmentRicky K. TairaRicky K. Taira Department of Radiological SciencesDepartment of Radiological Sciences
October 1, 1991 to October 1, 1991 to September 30, September 30,
19931993
July 1, 1993 to July 1, 1993 to June 30, 1997June 30, 1997
May 1, 1997 toMay 1, 1997 toApril 30, 2001April 30, 2001
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Research TeamStudentsJohn David N.
DionisioChih-Cheng HsuDavid JohnsonChristine Chih
CollaboratorsComputer Science
DepartmentAlfonso F. Cardenas
UCLA Medical SchoolDenise Aberle, MDRobert Lufkin, MDRicky K. Taira, MD
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SignificanceQuery multimedia data based on image content and spatial predicatesUse domain knowledge to relax and interpret medical queriesPresent integrated view of multiple temporal and evolutionary data in a timeline metaphor
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OverviewImage retrieval by feature and contentQuery relaxationSpatial query answeringSimilarity query answeringVisual query interfaceTimeline interfaceSample cases
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Image Retrieval by Content
Featuressize, shape, texture, density, histology
Spatial Relationsangle of coverage, shortest distance, overlapping ratio, contact ratio, relative direction
Evolution of Object Growthfusion, fission
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Characteristics of Medical Queries
MultimediaTemporalEvolutionarySpatialImprecise
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O O’01
Om
O O01
On
Evolution: Object O evolves into a new object O’
Fusion: Object 01, …, Om fuse into a new object
Fission: Object O splits into object 01, …, On
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Case a:
Case c:
The object exists with its supertype or aggregated type.
The life span of the object starts with and ends before its supertype or aggregated type.
Case b:
Case d:
The life span of the object starts after and ends with its supertype or aggregated type.
The life span of the object starts after and ends before its supertype or aggregated type.
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Lesion
Micro-Lesion Micro-
Lesion
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Query Modification Techniques
RelaxationGeneralizationSpecialization
Association
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Generalization and Specialization
More Conceptual Query
Specific Query
Conceptual Query Conceptual Query
Specific Query
Generalization
SpecializationGeneralization
Specialization
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Type Abstraction HierarchyPresents abstract view of
TypesAttribute valuesImage featuresTemporal and evolutionary behaviorSpatial relationships among objects
Provides multi-level knowledge representation
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TAH Generation for Numerical Attribute Values
Relaxation ErrorDifference between the exact value and the returned approximate valueThe expected error is weighted by the probability of occurrence of each value
DISC (Distribution Sensitive Clustering) is based on the attribute values and frequency distribution of the data
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TAH Generation for NumericalAttribute Values (cont.)
Computation Complexity: O(n2), where n is the number of distinct value in a clusterDISC performs better than Biggest Cap (value only) or Max Entropy (frequency only) methodsMDISC is developed for multiple attribute TAHs. Computation Complexity: O(mn2), where m is the number of attributes
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Query Relaxation
RelaxAttribute
Query
Yes
Display
QueryModification
AnswersDatabase
TAHs
No
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Cooperative Querying for Medical Applications
QueryFind the treatment used for the tumor similar-to (loc, size) X1 on 12 year-old Korean males.
Relaxed QueryFind the treatment used for the tumor Class X on preteen Asians.
AssociationThe success rate, side effects, and cost of the treatment.
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Type Abstraction Hierarchies forMedical Domain
Age
Preteens
910 1112
Teen Adult
Ethnic Group
Asian
Korean Chinese Japanese Filipino
African European
Tumor (location, size)
Class X[loc1 loc3]
[s1 s3]
Class Y[locY sY]
X1
[loc1 s1]
X2
[loc2 s2]
X3
[loc3 s3]
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Knowledge-Based Image Model
Representation Level(features and contents)
Brain Tumor LateralVentricle
TAHSR(t,b)
TAHTumor Size
TAHSR(t,l)
TAHLateral
Ventricle
SR: Spatial Relationb: Braint: Tumorl: Lateral Ventricle
Knowledge Level
Schema LevelSR(t,b) SR(t,l)
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KnowledgeBasedQueryProcessing
Queries
Query Analysis andFeature Selection
Knowledge-BasedContent Matching
Via TAHs
Query Relaxation
Query Answers
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User ModelTo customize query conditions and
knowledge-based query processing
User typeDefault Parameter ValuesFeature and Content Matching Policies
Complete MatchPartial Match
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User Model (cont.)
Relaxation Control PoliciesRelaxation OrderUnrelaxable ObjectPreference List
Measure for Ranking
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Query PreprocessingSegment and label contours for objects of interestDetermine relevant features and spatial relationships (e.g., location, containment, intersection) of the selected objectsOrganize the features and spatial relationships of objects into a feature databaseClassify the feature database into a Type Abstraction Hierarchy (TAH)
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Similarity Query Answering
Determine relevant features based on query inputSelect TAH based on these featuresTraverse through the TAH nodes to match all the images with similar features in the databasePresent the images and rank their similarity (e.g., by mean square error)
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Spatial Query AnsweringPreprocessing
Draw and label contours for objects of interestDetermine relevant features and spatial relationships (e.g., location, containment, intersection) of the selected objectsOrganize the features and spatial relationships of objects into a feature databaseClassify the feature database into a type abstraction hierarchy (TAH)
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Spatial Query Answering (cont.)Processing
Select TAH based on t he query conditions and contextSearch nodes to match the query conditionsReturn images linked to the TAH node
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Similarity Query AnsweringPreprocessing
Select objects and specify features of interest in the imageCreate a feature database of the selected objects for all imagesClassify the feature databases as type abstraction hierarchies
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Similarity Query Answering (cont.)
ProcessingDetermine relevant features based on query inputSelect TAH based on these features (interact with user to resolve ambiguity)Traverse through the TAH nodes to match all the images with similar features in the databasesPresent the images and rank their similarity (e.g., by mean square error)
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Visual Query Language and Interface
Point-click-drag interfaceObjects may be represented iconicallySpatial relationships among objects are represented graphically
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Visual Query Example
Retrieve brain tumor cases where a tumor is located in the region as indicated in the picture
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ImplementationSun Sparc 20 workstations (128 MB RAM, 24-bit frame buffer)Oracle Database Management SystemX/Motif Development Environment, C++Mass Storage of Images (9 GB)
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ConclusionsImage retrieval by feature and contentMatching and relaxation images based on featuresProcessing of queries based on spatial relationships among objectsAnswering of imprecise queriesExpression of queries via visual query languageIntegrated view of temporal multimedia data in a timeline metaphor
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Semi-Automatic Segmentation of Lung Tumors
classification seedestimation
adaptive fusion
regiongrowing
tumorsegment
interesting area
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