1. Detection of clusters of small features such as microcalcifications
BREAST EXPERTS
Baris Ozyer, ANKARA
Fatih Titrek, ANKARA
László Csernetics, SZEGED
Levente Ficsór, BUDAPEST
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2. OUTLINE The Problem Digital Mammography Literature
Steps to solve the problem.
If we could have more time….
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3. The Problem
Detection of clusters of small features such as circles or microcalcification on a noisy features background
Input: Given an image like a mammogram with the presence of microcalcifications of different size and shape which can be introduced by simulation for the purpose of this project
Output: Images with detection indicated
SSIP 2005

4. Digital Mammography
Definition: Digital mammography is a mammography system where x-ray film is replaced by solid-state detectors that convert x-rays into electric signals.
The electrical signals are used to produce images of the breast that can be seen on a computer screen or printed on special films to look like regular mammograms.
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5. The Advantages of the Digital Mammography
Fewer patient calls back for additional images.
Less anxiety for patience
Less time for doctors
The doctors can electronically manipulate images
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6. Used Programmes to improve the results
Matlab
Khoros
C++
ImageJ
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7. Some words about the problem…
Some problems to detect the microcalcifications on the images
High variety of microcalcifications
High variability of background
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8. Literature
Detection of Microcalcifications in Digital Mammograms Using Wavelets, Ted C. Wang and Nicolaos B. Karayiannis
“tophat” algorithm was applied to obtain unique markers for
Opening, Subtraction,Thresholding
The tophat algorithm is a morphological transform that is used to extract either locally bright or locally dark objects, with the use of shape information and relative brightness.
The numerical analysis of the detected microcalcifications
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9. More Literatures
Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Papadopoulosa, D.I. Fotiadisb, A. Likas, 2005
Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural, Rafayah Mousa, Qutaishat Munib, Abdallah Moussa, 2004
Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks, L. Bocchi, G. Coppini, J. Nori, G. Valli, 2003
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10. What is our plan ? Normalization to enhance contrast Smoothing
Morphological edge detection
Threshold
“Finding rings”
Measure the detected objects
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11. Normalization to enhance constrast
Typically normalization is attempting to remove global effects, that can be seen by examining plots that show all the data for a slide or slides.
Normalization does not necessarily have anything to do with the normal distribution that plays a prominent role in statistics.
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12. Smoothing
Smoothing is the process of taking an image and blurring it so that it looks out of focus. To find the edge it is better use smoothing technique.
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13. Morphological edge detection
The simply; difference between a dilated and an eroded image could be define an edge.
Smoothed Image
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Detected Image

14. Tresholding
The segmentation is determined by a single parameter known as the intensity threshold. In a single pass, each pixel in the image is compared with this threshold. If the pixel's intensity is higher than the threshold, the pixel is set to, say, white, in the output. If it is less than the threshold, it is set to black.
Detected Image
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Tresholded Image

15. Tresholding & Detecting the circle
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16. Measurements
Some kind of microcalcifications
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17. Further improvement in segmentation
Local normalization of image parts
Segment by homogeneity and intensity
Normalization for the current region
Microcalcification detection (as mentioned before)
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18. Further improvement in segmentation
Segmentation by homogeneity and intensity
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19. Further improvement in segmentation
Extract the current region
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20. Further improvement in segmentation
Normalization for the current region and edge detection
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21. Thanks for your attentions Questions if any are welcome
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