Feature Selection Mammogram based on Breast Cancer Mining
PRESENTATION_LOUIS_PAPAGEORGIOU_EN
1. « Endometrial cancer characterization
based on computer-aided microscopy
and pattern recognition methods »
« Endometrial cancer characterization
based on computer-aided microscopy
and pattern recognition methods »
Louis Papageorgiou, Spiros Kostopoulos,Louis Papageorgiou, Spiros Kostopoulos,
Dimitris Glotsos, Panagiota Ravazoula,Dimitris Glotsos, Panagiota Ravazoula,
Dionisis CavourasDionisis Cavouras
ATHENSATHENS 20132013
Department of Informatics and Telecommunications, University of Athens, GreeceDepartment of Informatics and Telecommunications, University of Athens, Greece
Medical Image and Signal Processing Laboratory, Department of MedicalMedical Image and Signal Processing Laboratory, Department of Medical
Instruments Technology, Technological Educational Institute of Athens, GreeceInstruments Technology, Technological Educational Institute of Athens, Greece
Department of Pathology, University Hospital of Patras, Rio, GreeceDepartment of Pathology, University Hospital of Patras, Rio, Greece
2. - Contents -- Contents -
• Introduction
• Endometrial cancer
• Process description
• Digital image processing
• Features extraction
• Pattern recognition
• Results
• Conclusions
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3. Introduction
The objective of the foreclosed thesis is the study
and development of a system which receives,
processes and analyses histopathological
microscopical images.
• This system developed to assist / support the
diagnosis of endometrial cancer.
• The support of diagnosis begins, presenting the
region of interest (ROI) where detected the
endometrial cancer into the images.
• Then using features and pattern recognition
techniques, images characterized into three grades
of endometrial cancer (grade I, II or III).
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4. Endometrial CancerEndometrial Cancer
• One of the most serious health
problems occurring throughout
the world.
• 7.6 million people worldwide
died, 13% causes of death.
• In 2008 diagnosed 12.7 million
several cases.
• Until 2030 expected to reach 13.1
million deaths.
• One of the most common cancer
cause of death in female sex are
Gynecological cancers.
CancerCancer
Endometrial cancer is one of the
frequent gynecological cancers.
Grows up in the outer layer of the
uterus.
Endometrial cancerEndometrial cancer
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5. Endometrial Cancer
Endometrial cancer biopsies, analyzed immuno-
histochemically by histopathologist. Oncogene cerb-B
expressed where nuclei stained brown and the other
nuclei colored blue-violet due to the hematoxylin stain.
DiagnosisDiagnosis
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9. Digital image processingDigital image processing
Separate region of interest (ROI) from histopathological
microscopical images using several techniques.
Overlapped coreOverlapped core
separationseparation
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10. Digital image processingDigital image processing
Morphological image processing, ROIs detectionMorphological image processing, ROIs detection
Example fromExample from 4040xx lenslens Example fromExample from 2020xx lenslens
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11. Greyscale ImagesGreyscale Images
ExamplesExamples
OutputOutput
separate binaryseparate binary
imagesimages
Digital image processingDigital image processing
Mapping regions of interestMapping regions of interest
Step IStep I ««Image processingImage processing»» exampleexample
1010
Region of interest (nuclei's ) separation from imageRegion of interest (nuclei's ) separation from image
12. Digital image processingDigital image processing
ROIs (nuclei) delimitation andROIs (nuclei) delimitation and improvement. Lapped nuclei separation if neededimprovement. Lapped nuclei separation if needed
Step IIStep II ««ROIs ProcessingROIs Processing»» examplesexamples
Greyscale ImagesGreyscale Images Binary imagesBinary images
from Step Ifrom Step I
SeparationSeparation Output ImagesOutput Images
NoNo
YesYes
YesYes
YesYes
YesYes
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13. Digital image processingDigital image processing
Searching for possible separation in a big ROIs (more than two overlappedSearching for possible separation in a big ROIs (more than two overlapped
nuclei's)nuclei's)
Step IIIStep III ««Big ROIs ProcessingBig ROIs Processing»» examplesexamples
Greyscale imagesGreyscale images
Binary imagesBinary images
from Step IIfrom Step II
ResultsResults
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17. • Minimum Distance Classifier
(MDC)
• K-Nearest Neighbours (KNN)
• Least Squares Minimum
Distance Classifier (LSMDC)
• Linear Bayes Classifier (LBC)
• Probabilistic Neural Network
(PNN)
• Ensemble classifier
Pattern RecognitionPattern Recognition
Classes definitionClasses definition
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ClassifiersClassifiers
ClassClass ΑΑ
Endometrial cancer
grade I
ClassClass ΒΒ
Endometrial cancer
grade II
Class CClass C
Endometrial cancer
grade III
18. Pattern RecognitionPattern Recognition
• Estimate classifiers performance
for known's patterns using «leave
one out»
• Estimate classifiers performance
for unknown's patterns using
«External cross validation».
• Estimate ensemble classification
performance for unknown's
patterns using «External cross
validation».
• Select best method to classify
• Features selection using:
- Optimal techniques
- Non optimal techniques
• Classifiers training with
«leave one out» technique.
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21. ConclusionsConclusions
• Regions of interest separate successfully, employing digital
processing techniques.
• Results are promising for the development of such system,
with some modest modifications suitable for a clinical
environment.
• Best classification algorithm was found the PNN that gave 94%
of correct classification when non optimal feature selection
method were employed. The PNN scored around 96% overall
accuracy employing the ‘exhaustive search‘ and the ‘leave one
out’ methods.
•The proposed image analysis system proved capable of
classifying a ‘new’ image with an average accuracy of 82%
employing the ‘external cross validation’ method.
• PNN classifier was the best choice for the classification part of
the system, considering the time and memory cost.
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