Detection of clusters of small features such as microcalcifications BREAST EXPERTS Baris Ozyer, ANKARA Fatih Titrek, ANKARA László Csernetics, SZEGED Levente Ficsór, BUDAPEST 2019.05.13. SSIP 2005

OUTLINE The Problem Digital Mammography Literature Steps to solve the problem. If we could have more time…. 2019.05.13. SSIP 2005

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 2019.05.13. SSIP 2005

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. 2019.05.13. SSIP 2005

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 2019.05.13. SSIP 2005

Used Programmes to improve the results Matlab Khoros C++ ImageJ 2019.05.13. SSIP 2005

Some words about the problem… Some problems to detect the microcalcifications on the images High variety of microcalcifications High variability of background 2019.05.13. SSIP 2005

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 2019.05.13. SSIP 2005

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 2019.05.13. SSIP 2005

What is our plan ? Normalization to enhance contrast Smoothing Morphological edge detection Threshold “Finding rings” Measure the detected objects 2019.05.13. SSIP 2005

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. 2019.05.13. SSIP 2005

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. 2019.05.13. SSIP 2005

Morphological edge detection The simply; difference between a dilated and an eroded image could be define an edge. 2019.05.13. Smoothed Image SSIP 2005 Detected Image

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. 2019.05.13. Detected Image SSIP 2005 Tresholded Image

Tresholding & Detecting the circle 2019.05.13. SSIP 2005

Measurements Some kind of microcalcifications 2019.05.13. SSIP 2005

Further improvement in segmentation Local normalization of image parts Segment by homogeneity and intensity Normalization for the current region Microcalcification detection (as mentioned before) 2019.05.13. SSIP 2005

Further improvement in segmentation Segmentation by homogeneity and intensity 2019.05.13. SSIP 2005

Further improvement in segmentation Extract the current region 2019.05.13. SSIP 2005

Further improvement in segmentation Normalization for the current region and edge detection 2019.05.13. SSIP 2005

Thanks for your attentions Questions if any are welcome 2019.05.13. SSIP 2005