GLEM - Matrix to classification

To summarize the diagnostic or prognostic information in a GLEM or a 4D GLEM into a single continuous value, we have developed an extraction method that automatically learns the relevancy of each matrix element from a set of discovery images. This approach outperformed classical pre-defined features when applied to the most difficult set of 45 Brodatz texture pairs (which are often used for evaluation and comparison of texture methods). In our texture analysis studies, the adaptively extracted feature was further simplified to a dichotomous classification of the patient as have good or poor prognosis using a conventional classification method known as minimum Euclidean distance.

Publications of special interest

• Nielsen B et al., Low dimensional adaptive texture feature vectors from class distance and class difference matrices. IEEE Trans Med Imaging 2004;23(1):73- 84).
• Nielsen B, Albregtsen F, Danielsen HE. Statistical nuclear texture analysis in cancer research: a review of methods and applications. Crit Rev Oncog 2008;14:89-164.