Evaluation of a two-stage model for detection of abnormalities in digital mammograms

Che, Fengying; Fairhurst, Michael; Wells, C.P.; Hanson, Martin

doi:10.1049/ic:19960496

Search citation statements

Order By: Relevance

Paper Sections

Select...

Segmentation1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2006

2017

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A clustering algorithm was used for fully automated segmentation 15 . Similar to region growing technique, used a pixel-by-pixel K-means clustering method 16,17 for initial mass segmentation. The clustering process separates one or more disjoint objects within the ROIs, which were filled, grown in a local neighborhood, and eroded and dilated by morphological operators.…”

Section: Segmentationmentioning

confidence: 99%

Computerized Breast Cancer Detection System

Hemalatha¹,

Krishnan²,

Umashankar³

et al. 2014

Biosci., Biotechnol. Res. Asia

View full text Add to dashboard Cite

Breast cancer is a leading cause of death among women, and its incidence is rising. Mammography has been shown to be effective in screening asymptomatic women to detect occult breast cancer and to reduce mortality by as much as 30% in women aged between 50 and 69 years. Our objective is to develop a CAD system to automatically detect, analyze, and to classify the different features in mammographic images through image processing technique. The feasibility of the proposed approach was explored on the images, and the sensitivity rate is 89% and the specificity is 93%.

show abstract

Section: Segmentationmentioning

confidence: 99%