ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

Gaussian Mixture Model (GMM) for mass abnormalities segmentation in
mammographic images.
1. ABSTRACT
K.Djaroudib 1 , F.Derraz 2 , A.Taleb.Ahmed 3 and A. Zidani 4
This paper deals about mass segmentation problem. To solve this problem, we propose
a semi-automatic mass segmentation method based on Gaussian Mixture Model
(GMM). The GMM give us important information about intensities probability
distribution of the mammogram images. Our method can be split in two steps, in the
first step, the mass statistics and shapes are learned by determining important
parameters. In the second step, we incorporated the learned statistics and shapes to
performed the mass segmentation by using an Expectation Maximization (EM)
algorithm witch estimates best fitting of GMM parameters. We applied the proposed
algorithm to some challenging breast images in BIRADS database including poor
contrast tissue density (fatty, dense or granular) and the segmented mass done by our
algorithm is compared to segmentation carried by an expert radiologist by measuring
Dice coefficient.
2. INTRODUCTION
Breast cancer is the first cause of death by cancer at the women. To detect anomalies
(mass or micro-calcification) at an early stage helps to decrease mortality rate, and
Computer Aided Diagnosis (CAD) being an effective tool for radiologist [1].
Masses are characterized by their location, size, shape and margin [2][3] and the large
variation in size and shape in which masse can appear, make mass segmentation a
challenging task for researchers. In additional, at the most of cases, mammograms
exhibit poor image contrast tissue density (fatty, dense or granular), then tissue can
overlap with mass [4][5]. According to these problems, many mass segmentation
methods are developed [6][7][8]. Here we contribute and propose a semi-automatic
mass segmentation method based on Gaussian Mixture Model (GMM).
The major force of GMM is prior information given by initial parameters to iterative
approach [9][10]. Then our method can be split in two steps, in the first step and for
initialization, the mass statistics and shapes are learned by determining important
parameters. These are extracted in suspicious region ROIs, and a number of clusters
regions are given by K-mean algorithm. In the second step, we incorporated the learned
statistics and shapes to performed the mass segmentation by using an Expectation
1
LAMIH, UVHC, Valenciennes, France. LaSTIC, Computer departement, UHL, Batna, Algeria.
2
Doctor, LAMIH, UVHC, Valenciennes, France.
3
Professor, LAMIH, UVHC, Valenciennes, France.
4
Doctor, Conferences Master, LaSTIC Laboratoty, Computer departement, UHL, Batna, Algeria.