1996
DOI: 10.1117/12.235922
|View full text |Cite
|
Sign up to set email alerts
|

<title>Derivative-based feature saliency for computer-aided breast cancer detection and diagnosis</title>

Abstract: Derivative-based feature saliency techniques were used to define the best of 25 Laws texture features for the classification of 101 malignant mass and benign mass regions. Statistical and derivative-based saliency techniques were used to select the best size, shape, contrast, and Laws texture features for the mass model. Nine features were chosen to define the model, of which four have been used by other researchers. Using this model, the regions were classified using a multilayer perceptron neural network arc… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 7 publications
(9 reference statements)
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?