Mammographic image segmentation and risk classification using a novel texture signature based methodology

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6136 LNCS, Issue , 2010, Pages 526-533

  He, Wenda ^a   Denton, Erika R E ^b   Zwiggelaar, Reyer ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

^b NORFOLK AND NORWICH UNIVERSITY HOSPITAL   (United Kingdom)

Author keywords

breast classification; breast segmentation; mammography; parenchymal patterns; Tab r

Indexed keywords

BI-RADS; BREAST CANCER RISK; BREAST CLASSIFICATION; BREAST SEGMENTATION; BREAST TISSUES; BUILDING BLOCKES; CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CLINICAL RESEARCH; GROUND TRUTH DATA; MAMMOGRAPHIC; MAMMOGRAPHIC IMAGES; MODEL SELECTION; QUALITATIVE EVALUATIONS; REMOVE NOISE; RISK CATEGORIES; RISK CLASSIFICATION; SEGMENTATION RESULTS; VISUAL ASSESSMENTS;

DIGITAL IMAGE STORAGE; FEATURE EXTRACTION; MAMMOGRAPHY; RISK ASSESSMENT; RISK MANAGEMENT; RISK PERCEPTION; TEXTURES; X RAY SCREENS;

IMAGE SEGMENTATION;

EID: 77954650183     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13666-5_71     Document Type: Conference Paper

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