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Jordi Freixenet, Arnau Oliver, Joan MartíUniversity of Girona
{jordif, aoliver, joanm}@eia.udg.es
In this work we propose a new algorithm to classify breasts according their internal tissue. Moreover, and in contrast with most of the previous works, the classification follows the BIRADS standard. Our proposal consists on grouping those pixels with similar tissue by using a clustering algorithm. Subsequently, mammograms are classified by comparing textural and morphological features extracted from each cluster
Abstract
Automatic classification of breast density according BIRADS categories using a clustering approach
Methodology
Results
Reyer ZwiggelaarUniversity of Wales
rrz@aber.ac.uk
Josep PontJosep Trueta Hospital
rd.jpont@htrueta.scs.es
1) Breast SegmentationTwo-step algorithm: an adaptive threshold extracts the background and the annotations, while the pectoral muscle is segmented by using a region growing algorithm
2) Cluster those pixels with similar appearanceWe used Fuzzy C-Means with two seeds depicting fatty and dense tissue. Both seeds were initialized by using histogram information
3) Extract features for both clustersWe extracted two different sets of features: morphologic set (centre of masses, relative area, mean intensity) and textural set (derived from co-occurrence matrices)
4) Training the classifiersWe used two different classifiers: the k-Nearest Neighbours and the ID3 Decision Tree
68
1474B-I
17322522
B-III
121791764B-IV1728176284B-III10153982618B-II91332123757B-I
B-IVB-IIIB-IIB-IVB-IIB-IID3kNN
40159Low
7268
Dense
7442Dense47138Low
DenseLowID3kNN
We have proposed a novel approach to classify mammograms according their internal tissue, and into BIRADS categories. We tested it using MIAS and DDSM databases, obtaining an overall percentage of correct classification of 66% and 73% for kNN and ID3 classifiers when testing the MIAS database, and almost 80% for the Fuzzy combination when testing the DDSM database
Conclusions
Leave-one-woman-outWe used a leave-one-out procedure: each mammogram of the set was classified by using all the other mammograms except the opposite one (the other mammogram of the same woman) in the training setThe method was tested using 300 mammograms of the DDSM database, as well as 320 mammograms of the MIAS database, which were classified in BIRADS categories by an expert
Constructing the data
Testing the model
9183424B-I
2354305
B-III
171951593B-IV25401718253B-III7104983527B-II332042417B-I
B-IVB-IIIB-IIB-IVB-IIB-IID3kNN
49127Low
11047
Dense
10140Dense23103Low
DenseLowID3kNN
18207523522238124832332123
B-IVB-IIIB-IIB-IFc (kNN,ID3)
1133726124
DenseLowFc (kNN,ID3)
Test Image
Database of mammograms already classified
Tissue Classification
BIRADS
Training Step
Classification Step
Classifier
or
ID3
kNN
Classifier Approach
MIA
S
DD
SM
Feat_C1 Feat_C2
0) Database of already classified mammogramsThe method needs as starting point a subset of previously classified mammograms
3( ) ( ) (1 ) ( )FzC i kNN i ID iP mammo B P mammo B P mammo Bα α∈ = ∈ + − ∈1( )
( , )i
kNN ij Bj kNN
P mammo Bdist j mammo∈
∈
∈ = ∑
3
( )( , )
kj
k Nodes ID mammo
wP mammo leafdist feat feat
∈ = ∑
3( ) ( )i
ID i jj B
P mammo B P mammo leaf∈
∈ = ∈∑
( ) ( )
5) Fuzzy combination of both classifiersIn some cases, both classifiers give us complementary results. Hence, we constructed a new classifier which takes advantage of both approaches by converting this classifier approach into a probabilistic framework
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