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Breast Cancer - FACTS:Breast Cancer - FACTS:Breast Cancer - FACTS:Breast Cancer - FACTS: Breast carcinoma leading Breast carcinoma leading
cause of cancer death in cause of cancer death in womeanwomean
Every 8-10Every 8-10thth woman woman affected affected during lifetimeduring lifetime
About 4000 new cases/a About 4000 new cases/a in in Austria Austria
Clustered Clustered mircrocalcifications one of mircrocalcifications one of early sign‘searly sign‘s
Breast Cancer - FACTS:Breast Cancer - FACTS:Breast Cancer - FACTS:Breast Cancer - FACTS:
0
1020
30
4050
6070
80
90100
Total local regional Metastasis unstaged
SURVIVAL vs TUMORSPREAD
Mammography - Mammography - REPORTING:REPORTING:
Mammography - Mammography - REPORTING:REPORTING:
Clsutered Microcalcifications are Clsutered Microcalcifications are early sign´s ofbreast cancer early sign´s ofbreast cancer (bright spot´s)(bright spot´s)- Size: 0.2-0.5 mm - search with a Size: 0.2-0.5 mm - search with a
magnifying glassmagnifying glass- Difficult perception in low contrast areasDifficult perception in low contrast areas- Differentiate: cancerous vs non Differentiate: cancerous vs non
cancerouscancerous
Mammography -Mammography -DILEMMA: DILEMMA:
Mammography -Mammography -DILEMMA: DILEMMA:
Special training of radiologists Special training of radiologists necessary necessary
Positive predictive value of Positive predictive value of radiologists: 20%radiologists: 20%
„ „Double Reading“ (independet Double Reading“ (independet reporting by 2 reporting by 2 radiologists): radiologists): improves accuracy by 5 - 15%improves accuracy by 5 - 15%
Artificial Intelligence in Artificial Intelligence in Mammography - Mammography -
HYPOTHESIS:HYPOTHESIS:
Artificial Intelligence in Artificial Intelligence in Mammography - Mammography -
HYPOTHESIS:HYPOTHESIS:
Clustered microcalcifications Clustered microcalcifications be found reliably found by a be found reliably found by a computer application computer application
and a discrimination between and a discrimination between cancerous and non cancerous cancerous and non cancerous microcalcifications be done microcalcifications be done using neuronal netsusing neuronal nets
Mammography - Mammography - VISIONVISION
Mammography - Mammography - VISIONVISION
CAD System CAD System ((CComputer omputer AAided ided DDiagnosis) can act as iagnosis) can act as “never tired, second “never tired, second reader”reader”
Variance of female Variance of female FEATURESFEATURES
Variance of female Variance of female FEATURESFEATURES
ANN & Mammography - ANN & Mammography - PATIENTS: PATIENTS:
ANN & Mammography - ANN & Mammography - PATIENTS: PATIENTS:
Patient Database:Patient Database:- 100 patients = 272 Images 100 patients = 272 Images
with Mc´swith Mc´s- High resolution film High resolution film
digitalization: digitalization: 8000x6000x15 -> about 90 8000x6000x15 -> about 90 Mbyte/imageMbyte/image
- Resolution for image Resolution for image processing: 91.5 processing: 91.5 mm
ANN & Mammography - ANN & Mammography - GROUNDTRUTH:GROUNDTRUTH:
ANN & Mammography - ANN & Mammography - GROUNDTRUTH:GROUNDTRUTH:
All patients operated All patients operated and biopsy and biopsy reports reports availabale:availabale:- 54 malignant, 46 benign54 malignant, 46 benign
All patients rated to be All patients rated to be - benign, indeterminate, benign, indeterminate,
malignantmalignant Manual marking of Manual marking of
- 828 indiv. microcalcifications828 indiv. microcalcifications- 735 artifacts735 artifacts
ANN & Mammography ANN & Mammography - -
HARD & SOFT:HARD & SOFT:
ANN & Mammography ANN & Mammography - -
HARD & SOFT:HARD & SOFT: Hardware:Hardware:
- SunSparc20SunSparc20- Neurocomputer Synapse-1 (SNAT):Neurocomputer Synapse-1 (SNAT):
Software: Software: - Image Processing: IDL 5.0, Creaso Image Processing: IDL 5.0, Creaso
Research SystemResearch System- ANN - Synapse-1: ANN - Synapse-1: nneuronal euronal
AApplication pplication LLanguageanguage(SNAT, Germany) - C++ libraries (SNAT, Germany) - C++ libraries
ANN & Mammo - ANN & Mammo - WORKFLOWWORKFLOW
ANN & Mammo - ANN & Mammo - WORKFLOWWORKFLOW
Backgroundcorrection
Classification:
Feat.Extr. ANN
malign
benignindeterminate
ANNtypical
Detection:
ANNFeature Extraction
ANN & Mammography -ANN & Mammography -BACKGROUNDKORRECTIOBACKGROUNDKORRECTIO
NN
ANN & Mammography -ANN & Mammography -BACKGROUNDKORRECTIOBACKGROUNDKORRECTIO
NN
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION: Line feature:Line feature:
- Calculate Sobel GradientsCalculate Sobel Gradients- map to 16 directionsmap to 16 directions- make histogramm 9x9 make histogramm 9x9 - if difference between 2 peaks > 6/16 if difference between 2 peaks > 6/16
and < 10/16and < 10/16 multiply both peaks = line multiply both peaks = line
feature for a pointfeature for a point
Linefeatures:Linefeatures:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
Linefeatures:Linefeatures:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
Linefeatures:Linefeatures:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
DETECTION:DETECTION:
ANN & Mammography -ANN & Mammography -RESULTS DETECTION:RESULTS DETECTION:
ANN & Mammography -ANN & Mammography -RESULTS DETECTION:RESULTS DETECTION:
ANN & Mammography -ANN & Mammography -FEATURES FOR CLASSIFICATIONFEATURES FOR CLASSIFICATION
ANN & Mammography -ANN & Mammography -FEATURES FOR CLASSIFICATIONFEATURES FOR CLASSIFICATION
Mean vector of population: Mean vector of population: mmxx = = EE{{xx}}
Covariance matrix: Covariance matrix: CCxx = = EE{({(x x - - mmxx)()(x x - - mmxx))TT}}
Calculate eigenvectors of Calculate eigenvectors of CCx x , and , and transformationmatrix Atransformationmatrix A
Transform image: Transform image: yy = = AA((x x - - mmxx) - ) - Hotelling Hotelling TransformationTransformation
ANN & Mammography -ANN & Mammography -FEATURES FOR CLASSIFICATIONSFEATURES FOR CLASSIFICATIONS
ANN & Mammography -ANN & Mammography -FEATURES FOR CLASSIFICATIONSFEATURES FOR CLASSIFICATIONS
Extension in 8 directionsExtension in 8 directions „ „Minimum Enclosing Minimum Enclosing
Rectangle“:Rectangle“:- Center of gravity, excentricity, Center of gravity, excentricity,
aspect-ratio ....... aspect-ratio .......
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS
Features of individual Features of individual microcalcifications:microcalcifications:- Meanvalue of greylevels of pixels within Meanvalue of greylevels of pixels within
one mcone mc- Local contrast of one mcLocal contrast of one mc- Border gradientsBorder gradients- Area, perimeter and compactness (PArea, perimeter and compactness (P22/A)/A)
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS Clustering - recursive Clustering - recursive algorithmalgorithm
Area of the cluster - Convex Area of the cluster - Convex Hull ProcedureHull Procedure
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS Features from „Convex Features from „Convex
Hull“:Hull“:- Number of mc’sNumber of mc’s- Area, perimeter and compactness of Area, perimeter and compactness of
clustercluster- Density (number of mc’s/A)Density (number of mc’s/A)- Inter mc - distancesInter mc - distances- descriptive statistics of individual mc descriptive statistics of individual mc
featuresfeatures
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS
ANN & Mammography -ANN & Mammography -FEATURES FOR FEATURES FOR
CLASSIFICATIONSCLASSIFICATIONS Total features: n=73Total features: n=73 Automatic Automatic
selectionsprocess: selectionsprocess: patient based patient based „ Leave-one-Out-Test“ „ Leave-one-Out-Test“ for every featurefor every feature
10 suited for differentiation typical - 10 suited for differentiation typical - indeterminateindeterminate
10 suited for differentiation typical - 10 suited for differentiation typical - indeterminateindeterminate
12 suited for differentiation benign - 12 suited for differentiation benign - malignantmalignant
12 suited for differentiation benign - 12 suited for differentiation benign - malignantmalignant