Machine vision in the detection of prostate lesions in histologic sections

OBJECTIVE: To explore the utility of N-gram encoding for the automated detection and delineation of regions of histologic abnormality in tissue sections of prostate. STUDY DESIGN: Digitized imagery of tissue sections from normal prostate glandular tissue, stroma and regions of well- and poorly differentiated lesions was recorded and successively subdivided into square subregions of 256 x 256 to 16 x 16 pixels. N-grams of N = 2 to N = 6 were computed, with each element assuming a value representing an optical density interval 0.30 units wide, covering the range from optical density = 0.0 to 1.80. Then, from a large database, prototype frequency histograms of the different N-grams were established. For each subregion the Euclidean distances to the different prototype histograms were computed and defined as "distance to prototype" features. Standard discriminant analyses and a nonparametric classifier were used to assign subregions to the different tissue categories. RESULTS: Classification of subregions was achieved for most discrimination tasks at a correct recognition rate ranging from 85% to 100% on both training set and test set data, with a few exceptions. N-grams of N > 4 had considerable discriminatory power. CONCLUSION: N-gram encoding has the potential to provide highly discriminating, texture-based characterization of subregions of digitized imagery of prostate lesions and may be very useful in the development of decision procedures for the automated detection of prostate lesions by a machine vision system.