Manual identification of capillaries in transverse muscle sections is laborious and time consuming. Although the process of classifying a structure as a capillary is facilitated by (immuno)histochemical staining methods, human judgement is still required in a significant number of cases. This is mainly due to the fact that not all capillaries stain as strongly: they may have an elongated appearance and/or there may be staining artefacts that would lead to a false identification of a capillary. Here we propose two automated methods of capillary detection: a novel image processing approach and an existing machine learning approach that has been previously used to detect nuclei-shaped objects. The robustness of the proposed methods was tested on two sets of differently stained muscle sections. On average, the image processing approach scored a True Positive Rate of 0.817 and a harmonic mean (F1 measure) of 0.804 whilst the machine learning approach scored a True Positive Rate 0.843 and F1 measure of 0.846. Both proposed methods are thus able to mimic most of the manual capillary detection, but further improvements are required for practical applications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.