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An Automatic Mass Detection System in
Mammograms based on Complex Texture
Features
Abstract—It is difficult for radiologists to identify the masses on a mammogram because they
are surrounded by complicated tissues. In current breast cancer screening, radiologists often miss

2. approximately 10% - 30% of tumors because of the ambiguous margins of lesions and visual
fatigue resulting from long-time diagnosis. For these reasons, many computer-aided detection
(CADe) systems have been developed to aid radiologists in detecting mammographic lesions
which may indicate the presence of breast cancer. This study presents an automatic CADe
system that uses local and discrete texture features for mammographic mass detection. This
system segments some adaptive square regions of interest (ROIs) for suspicious areas. This study
also proposes two complex feature extraction methods based on co-occurrence matrix and optical
density transformation to describe local texture characteristics and the discrete photometric
distribution of each ROI. Finally, this study uses stepwise linear discriminant analysis to classify
abnormal regions by selecting and rating the individual performance of each feature. Results
show that the proposed system achieves satisfactory detection performance.
Existing method:
A radiologist typically examines a mammogram to check for signs of cancer. Computer-aided
detection (CADe) system prompts the radiologist to re-examine the films. When using a CADe

3. system with mammography, a radiologist still reads the mammogram, but a computer program
also evaluates the mammogram and highlights suspicious regions for the radiologist to review.
Finally, the radiologist identifies true areas of concern before making a final diagnosis.

4. Proposed method
The proposed scheme first provides a preprocessing step to preserve the breast area and eliminate
the structural noises in the mammograms. Then, suspicious regions are selected from the breast
area using the Sech template matching method, and adaptive square regions of interest (ROIs)
are segmented from the original mammogram corresponding to suspected regions.

5. Results
Fig.1.(a) Original adaptive ROIs containing a mass respectively, (b) grey level images of
corresponding object regions to (a), and (c) optical density images of corresponding object
regions to (a).