Τίτλος:
Breast cancer characterization based on image classification of tissue sections visualized under low magnification
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I-III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination. © 2013 C. Loukas et al.
Συγγραφείς:
Loukas, C.
Kostopoulos, S.
Tanoglidi, A.
Glotsos, D.
Sfikas, C.
Cavouras, D.
Περιοδικό:
Computational and Mathematical Methods in Medicine
Λέξεις-κλειδιά:
Diseases; Medical imaging; Pattern recognition systems; Support vector machines; Textures; Tissue, Architectural pattern; Breast cancer diagnosis; Classification accuracy; Pattern classifier; Pattern recognition algorithms; Rapid assessment; Regions of interest; Visual examination, Image classification, article; artificial neural network; automation; breast biopsy; breast cancer; cancer classification; cancer tissue; classification algorithm; clinical assessment; controlled study; diagnostic accuracy; diagnostic test accuracy study; disease severity; histopathology; human; human tissue; image analysis; image reconstruction; k nearest neighbor; pattern recognition; predictive value; probabilistic neural network; support vector machine; tissue section; tumor volume; algorithm; automated pattern recognition; biopsy; Breast Neoplasms; cancer grading; classification; computer assisted diagnosis; female; pathology; procedures; statistics and numerical data; automated pattern recognition; breast tumor; computer assisted diagnosis; methodology; pathology; statistics, Algorithms; Biopsy; Breast Neoplasms; Diagnosis, Computer-Assisted; Female; Humans; Image Interpretation, Computer-Assisted; Neoplasm Grading; Pattern Recognition, Automated; Support Vector Machines, Algorithms; Biopsy; Breast Neoplasms; Diagnosis, Computer-Assisted; Female; Humans; Image Interpretation, Computer-Assisted; Neoplasm Grading; Pattern Recognition, Automated; Support Vector Machines
