Computerized detection of malignant tumors on digital mammograms

This paper presents a tumor detection system for fully digital mammography. The processing scheme adopted in the proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract feat...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 1999-05, Vol.18 (5), p.369-378
Main Authors: Kobatake, H., Murakami, M., Takeo, H., Nawano, S.
Format: Article
Language:English
Subjects:
Adaptive filtering
Adaptive filters
Algorithms
Biological and medical sciences
Breast cancer
Breast Neoplasms - diagnostic imaging
Computer aided diagnosis
Diagnosis, Computer-Assisted - instrumentation
Diagnosis, Computer-Assisted - methods
Diagnosis, Computer-Assisted - statistics & numerical data
Diagnosis, Differential
Digital filters
Digital signal processing
Diseases
Evaluation Studies as Topic
False Negative Reactions
False Positive Reactions
Feature extraction
Female
Filtration - instrumentation
Filtration - methods
Gynecology. Andrology. Obstetrics
Humans
Iris
Large-scale systems
Malignant tumors
Mammary gland diseases
Mammography
Mammography - instrumentation
Mammography - methods
Mammography - statistics & numerical data
Medical sciences
Neoplasms
Radiographic Image Enhancement - instrumentation
Radiographic Image Enhancement - methods
Sensitivity and Specificity
System performance
Tumors
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Summary:This paper presents a tumor detection system for fully digital mammography. The processing scheme adopted in the proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which characterize malignant tumors. For the first problem, a unique adaptive filter called the iris filter is proposed. It is very effective in enhancing approximately rounded opacities no matter what their contrasts might be. Clues for differentiation between malignant tumors and other tumors are believed to be mostly in their border areas. This paper proposes typical parameters which reflect boundary characteristics. To confirm the system performance for unknown samples, large scale experiments using 1212 CR images were performed. The results showed that the sensitivity of the proposed system was 90.5% and the average number of false positives per image was found to be only 1.3. These results show the effectiveness of the proposed system.
ISSN:0278-0062
1558-254X
DOI:10.1109/42.774164