Abstract
Image-based morphometry is an important area of pattern recognition research, with numerous applications in science and technology (including biology and medicine). Fisher linear discriminant analysis (FLDA) techniques are often employed to elucidate and visualize important information that discriminates between two or more populations. We demonstrate that the direct application of FLDA can lead to undesirable errors in characterizing such information and that the reason for such errors is not necessarily the ill conditioning in the resulting generalized eigenvalue problem, as usually assumed. We show that the regularized eigenvalue decomposition often used is related to solving a modified FLDA criterion that includes a least-squares-type representation penalty, and derive the relationship explicitly. We demonstrate the concepts by applying this modified technique to several problems in image-based morphometry, and build discriminant representative models for different data sets.
Highlights
In biology and medicine, morphology refers to the study of the form, structure and configuration of an organism and its component parts
We show that a modified Fisher Linear Discriminant Analysis (FLDA) criterion that includes a representation penalty error can be used in such cases to extract meaningful discriminating information
We can see the method we propose does recover the correct information that discriminates between the two populations. While this is not necessarily the most discriminating information in the FLDA sense, it is the most discriminating information that is well populated by the data, in the sense made explicit by equation (9)
Summary
Morphology refers to the study of the form, structure and configuration of an organism and its component parts. Many researchers working in applications in medicine and biology have shifted to a more geometric approach, where the entire morphological exemplar (as depicted in an image) is viewed as a point in a carefully constructed metric space [5, 6, 7], often facilitating visualization. When a linear embedding for the data can be assumed, standard geometric data processing techniques such as principal component analysis can be used to extract and visualize major trends in the morphologies of organs and cells [8, 9, 10, 11, 7, 12, 13, 14, 15]. While representation of summarizing trends is important, so is the application of discrimination techniques for elucidating and visualizing trends that differentiate between two or more populations [16, 17, 18, 19, 20, 21]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
