The CALMA project

A CAD tool in breast radiography

A.Ceccopieri, Padova 9-2-2000

CComputer omputer AAssisted ssisted LLibrary in ibrary in MAMAmmographymmography

Screening mammographysensitivity (identified positives / true positives) 73% - 88%specificity (identified negatives / true negatives) 83% - 92%

These merit figures INCREASE if diagnosis is performed by 2 independent radiologists

CALMA aims to:•Build a DATABASE of mammograms in digital format•Perform an automatic classification of parenchyma structures•Detect the spiculated lesions•Detect micro-calcification clusters

900 patients

2900 imagesGlandular58 %

DN5 %

FA37 %OUR DATABASE

DAQ: granularity: 85 m

range:12 bitdimensions: ~2000x2600 pixels

STORAGE60 images/ CD (no compression)

up to 240 CD

HARDWARE

DAQ panel& database

search

QueriesFull screen display

Preview and images’ description

Automatic classification of breast parenchyma

Left to right / top to bottom:

- dense (DN)- irregularly nodular (IN)- micro-nodular (MN)- fiber-adipose (FA)- fiber-glandular (FG)- parvi-nodular (PN)

-Glandular (IN+MN+FG+PN)

0 2 4 6 8 10 12 14 16

0

20

40

60

80

100 Trasformata di Fourier

F(k)

k

SupervisedFF-ANN

Spatial frequencies analysis (FFT)

512x512pixels analysis

ANN classification

Featureextraction

2dim FFT

GLANDULAR

RESULTS: RESULTS: TEXTURE ANALYSISTEXTURE ANALYSIS

DENSE ADIPOSE GLANDULAR

DENSE >95% 0% 0%

ADIPOSE 16% 68±3% 16%

GLANDULAR 4% 3% 93±1%

SPICULATED LESIONS

Unroll spirals

Spatial frequencies analysis(FFT)

FF-ANN

examples

0 50 100 150 200 250 300

160

170

180

190

200

210

220 Vettore Spiral

f(j)

j

0 2 4 6 8 10 12 14 16

0

20

40

60

80

100 Trasformata di Fourier

F(k)

k

Method Area (cm2) spread (cm2)

B (0-0) 31 16

B (1-3) 27 13

B (2-5) 25 13

C neural 36 12

C normalized 36 18

C corona 49 27

RESULTS @ sensitivity=90(±3)%:

Integration range 2-5

Spiculated lesions:CAD performances

Red= radiologist

Blue= CAD

RESULTS: RESULTS: SPICULATED LESIONSSPICULATED LESIONS

Sensitivity (per patient) 90±3%

FALSE POSITIVES / IMAGE 1.4

AVERAGE ROI 25 cm2

DATA REDUCTION ~ 10

Examples

MICROCALCIFICATION CLUSTERS

FF-ANN + Sanger learning rule

PCA

Method

•Image Preprocessing (convolution filters)

•PCA through a NN trained with the Sanger rule

•Study of the first Principal Components

•Classification

Preprocessing

• 60x60 pixels windows selection• convolution filters with dims:

5x5 7x7 9x9

Best results with a 7x7 filter with A=1\N2 aij <0 (aij kernel element)

Results

Sensitivity = 73 ± 2 % Specificity= 94 ± 2 %

With micro-calcification clusters

No Micro-calcification clusters

2

3

1

Micro-calcification clusters: CAD

Red= radiologist

Blue= CAD

RESULTS: RESULTS: MICRO-CALCIFICATION CLUSTERSMICRO-CALCIFICATION CLUSTERS

SENSITIVITY 73±2%

SPECIFICITY 94±2%

FUTURE• Software developement: 1- Local

classification of parenchyma 2- Use parenchyma classification for lesions CAD 3- Use the asymmetry between the two sides to detect cancer.

• Increase the DATABASE • “ON-LINE Validation”: Is CALMA a good

(second) radiologist? • Implementation of physician-friendly

CAD workstations in the collaborating Hospitals