Thesis presentation raeeda
•Download as PPT, PDF•
1 like•205 views
Feature Selection from Ultersound Image to Aid in Breast Cancer Detection
Recommended
More Related Content
What's hot
What's hot (17)
Viewers also liked
Viewers also liked (9)
Similar to Thesis presentation raeeda
Similar to Thesis presentation raeeda (20)
More from Shahrin Ahammad
Recently uploaded
Recently uploaded (20)
Thesis presentation raeeda
- 2. Group Members • Shahrin Ahammad Shetu • Farzin Raeeda Jamil • Md.A.Mannan Joadder • Mohd.Ashique Ridwan Nayeem • Supervisor: Abdullah Al Helal Department of EEE United International University
- 3. Outline Introduction Work-Flow Feature Extraction Feature Selection Classification Experiment & Results Conclusion
- 5. What is cancer ? Abnormal growth of cells Affects major body parts Results in degradation of the body and eventually death Second most major cause of death
- 6. Breast Cancer Second most lethal form of cancer in women Large number are affected and die every year Early detection helps fight cancer more effectively Lung & Bronchus 26% Breast CancerBreast Cancer 15%15% Colon & Rectum 9% Pancreas 7% Ovarian Cancer 6%
- 7. Mammography Mammography is the most common detection technique But uses ionizing radiation Expensive Works poorly in dense regions (in young women)
- 9. Challenges of Ultrasonography It requires interpretation by experts. Thus it is operator dependent. High inter-observer variation rate
- 10. CAD using ultrasonograms Computer Aided Diagnosis (CAD) tool may be used to detect malignant tumor. CAD is relatively operator independent. Reduces inter-observer variation rate Should have a high accuracy
- 11. CAD Steps
- 12. Objective Our aim was to improve CAD tool using feature selection method. Feature Selection and Feature Extraction are integral parts of CAD system. Feature selection is a relatively unexplored region. Large number of features will in aid feature selection.
- 14. Our Focus
- 15. Initial Steps Ultrasound scans Perform preprocessing like noise elimination Segmentation into different ROI Dr. Kaisar Alam from Riverside Research shared his ultrasound database of segmented images with us. 504 ultrasound scans (102 different patients) 50 were malignant, and the rest were benign.
- 17. Motivation Researchers proposed different types of features No one really extracted a large number of features Large number of features will assist feature selection We considered different proposed features We extracted a large pool of 58 features
- 18. Features Benign and malignant tumor have different features. A good feature has small difference for the same class, but large difference for two different classes. Benign tumor is oval shaped, smooth textured. Malignant tumors have irregular shape, rough Benign Tumor Malignant tumor
- 19. Features are extracted from segmented ultrasound scans. Three major categories Categories of Features
- 20. Morphological Features Describes tumor shape, boundary etc. Only the segment containing the lesion is considered. Malignant lesions have poorly defined margins or boundaries. Benign tumors have well defined margins. Malignant lesions are generally irregular in shape. Benign lesions have regular or oval shape.
- 21. Examples Perimeter Represents lesion perimeter Malignant tumors have relatively larger perimeter due to irregular boundary Thus larger value roughly indicates malignant tumor Area Represents lesion area Malignant lesions have larger areas Due to regular shape benign tumors have smaller area Thus smaller area indicates benign tumor
- 22. Acoustic Features Echogenicity Ability to bounce an echo Benign tumors are more echogenic compared to malignant tumors. Heterogeneity Defines uniformity in a substance Benign lesions are less heterogenic compared to malignant lesions.
- 23. Texture Features Based on textural property Considers the entire ultrasound scans It can detect variation of pixel intensities Can detect texture smoothness Malignant tumors generally have larger variance of intensities
- 24. Example Variance contrast • Ratio of variance of the inside and the outside of the tumor
- 26. What is Feature Selection ? A method by which useful features are selected It reduces the number of irrelevant features Also decreases the computational cost
- 28. Feature Selection Techniques Wrapper methods Filter methods We have used four techniques.
- 29. Wrapper Wrapper method considers that all combination of features are tested Thus has a high computational cost. Hence only forward and backward feature selection are considered
- 30. Laplacian score Filter method Works in both supervised and unsupervised condition Main concept is when two data are close to each other they refer to the same object
- 31. Supervised and Unsupervised Learning Functions from labeled training data Hidden structure in unlabeled training data
- 32. MCFS Multi-cluster feature selection Recently developed filter method Works for both unsupervised and supervised learning Measures correlation between different features without label information Performs better clustering and classification
- 33. Elastic Net Embedded method Mix of ridge and lasso Feature shrinking and selection
- 34. Why Feature Selection ? A gigantic feature set can cause a high computational cost Feature selection excludes irrelevant features Previously unexplored in cancer detection from ultrasonograms
- 36. What is Classification ? The method by which test tumor can be identified as benign or malignant It works based on the features returned from the selection method
- 37. Sparse Representation Classifier (SRC) We employed sparse representation classifier (SRC) This is a newly developed classifier SRC produced promising result in face recognition technique
- 38. Performance metric - Area Under The ROC Curve (AUC) Ideal : AUC = 100% Worst: AUC = 50% The higher AUC, the better
- 40. Data Set 504 pathologically proven ultrasound scans 454 benign tumors 50 malignant tumors Segmented into 9 ROIs
- 41. Feature Extraction Three different types of features A large number of features are needed for efficient feature selection A total of 58 different features was extracted.
- 42. Feature Extraction (contd.) Classification was carried out using all the features AUC = 87.52% AUC of 87.52%
- 43. Wrapper A subset of features was obtained This subset contained features from the original feature pool AUC = 88.09% AUC of 88.09% Feature Selection Techniques
- 44. Laplacian Score A subset of features was obtained This subset contained 48 features from the original features AUC = 90.37% AUC of 90.37%
- 45. MCFS MCFS produced the most promising feature subset An optimized subset of 25 features was obtained AUC = 93.31% AUC of 93.31%
- 46. Elastic Net A subset of features was obtained This subset contained 17 features from the original feature pool AUC = 89.91% AUC of 89.91%
- 48. We generated a large pool of 58 features that describe breast cancer. An optimized feature subset using feature selection technique was obtained. MCFS produced the most promising AUC of 93.31%. This work was recognized as a conference paper in 2014. Wrapping Up
- 49. The consistency of this method could be further enhanced using an even wider feature pool and more sophisticated feature selection technique. Automated feature learning technique could be employed. Future Work
- 50. With the advancement of technology over a period of time we as human beings have progressed enormously! We as engineers hope to use these technology and knowledge to try to fight for the positive to humankind and make this world a better place