A fusion-based approach for uterine cervical cancer histology image classification.
Expert pathologists commonly perform visual interpretation of histology slides for cervix tissue abnormality diagnosis. We investigated an automated, localized, fusion-based approach for cervix histology image analysis for squamous epithelium classification into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The epithelium image analysis approach includes medial axis determination, vertical segment partitioning as medial axis orthogonal cuts, individual vertical segment feature extraction and classification, and image-based classification using a voting scheme fusing the vertical segment CIN grades. Results using 61 images showed at least 15.5% CIN exact grade classification improvement using the localized vertical segment fusion versus global image features.
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409-0040, USA.
Soumya
S
De
R Joe
RJ
Stanley
Cheng
C
Lu
Rodney
R
Long
Sameer
S
Antani
George
G
Thoma
Rosemary
R
Zuna
D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D008867:Microtomy; D060787:Neoplasm Grading; D009369:Neoplasms; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D002583:Uterine Cervical Neoplasms / Q000473:pathology*; D002578:Uterine Cervical Dysplasia / Q000473:pathology*
Cervical intraepithelial neoplasia; Data fusion; Feature analysis; Histology; Image processing