The paper presents the results of application of different supervised (such as class pair-wise hyperplane learning and nearest neighbor) and unsupervised (such as distance based cluster analysis) classification techniques to cell imagery data using certain newly developed textural features. The effectiveness of these joint run length -- gray level distribution based textural descriptors as features for classification of cell image data both in supervised and unsupervised modes is illustrated with actual data drawn from four specific groups of cells: (1) lymphoma, (2) dermis collagen, (3) infiltrating lobular carcinoma, and (4) infiltrating scirrhous carcinoma. As is to be expected from theoretical considerations, supervised classification does indeed provide better results. However, even in the unsupervised classification mode, the results are very close to that obtained under the supervised mode, thus demonstrating the merits of the new textural features in the classification of cell imagery data. This is further confirmed through feature evaluation and assessment based on derivation of figures of merit for the discrimination potential of the newly defined textural features. Results of application of a recently proposed method of minimizing the training set for the application of nearest neighbor classifier are also presented to bring out the effectiveness of these textural features in terms of their ability to represent the different classes with very few training samples.

Date Published: 1 June 1992
PDF: 8 pages
Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); doi: 10.1117/12.59435

Published in SPIE Proceedings Vol. 1652:
Medical Imaging VI: Image Processing
Murray H. Loew, Editor(s)