Mammographic images segmentation using texture descriptors.
Tissue classification in mammography can help the diagnosis of breast cancer by separating healthy tissue from lesions. We present herein the use of three texture descriptors for breast tissue segmentation purposes: the Sum Histogram, the Gray Level Co-Occurrence Matrix (GLCM) and the Local Binary Pattern (LBP). A modification of the LBP is also proposed for a better distinction of the tissues. In order to segment the image into its tissues, these descriptors are compared using a fidelity index and two clustering algorithms: k-Means and SOM (Self-Organizing Maps).
Mascaro
Angelica A
AA
Center of Informatics, Federal University of Pernambuco, Recife, Brazil. aam3@cin.ufpe.br
Mello
Carlos A B
CA
Santos
Wellington P
WP
Cavalcanti
George D C
GD
D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D016000:Cluster Analysis; D003201:Computers; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008327:Mammography / Q000295:instrumentation / Q000379:methods*; D008495:Medical Oncology / Q000295:instrumentation / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software