Next Article in Journal
Hearing Rehabilitation with Cochlear Implants after CyberKnife Radiosurgery of Vestibular Schwannoma: A Report Based on Four Clinical Cases
Previous Article in Journal
Is There a Causal Link between the Left Lateralization of Language and Other Brain Asymmetries? A Review of Data Gathered in Patients with Focal Brain Lesions
Previous Article in Special Issue
FARCI: Fast and Robust Connectome Inference
Article

The Active Segmentation Platform for Microscopic Image Classification and Segmentation

1
Visual and Data-Centric Computing, Zuse Institute Berlin (ZIB), Takustrasse 7, 14195 Berlin, Germany
2
Neuroscience Research Flanders (NERF), Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, 3001 Leuven, Belgium
3
Information Technologies for Sensor Data Processing (ITSDP) Department, Institute of Information and Communication Technologies (IICT), Bulgarian Academy of Sciences, Acad. G. Bonchev St., Block 25A, 1113 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Academic Editor: Alan Chuck Dorval
Brain Sci. 2021, 11(12), 1645; https://doi.org/10.3390/brainsci11121645
Received: 30 November 2021 / Accepted: 7 December 2021 / Published: 14 December 2021
(This article belongs to the Special Issue Neuroinformatics and Signal Processing)
Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects. View Full-Text
Keywords: interactive segmentation; random forest; support vector machines; features exploration; scale space; differential invariants; legendre moments; zernike moments interactive segmentation; random forest; support vector machines; features exploration; scale space; differential invariants; legendre moments; zernike moments
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.5771719
    Description: Manual segmentation dataset
MDPI and ACS Style

Vohra, S.K.; Prodanov, D. The Active Segmentation Platform for Microscopic Image Classification and Segmentation. Brain Sci. 2021, 11, 1645. https://doi.org/10.3390/brainsci11121645

AMA Style

Vohra SK, Prodanov D. The Active Segmentation Platform for Microscopic Image Classification and Segmentation. Brain Sciences. 2021; 11(12):1645. https://doi.org/10.3390/brainsci11121645

Chicago/Turabian Style

Vohra, Sumit K., and Dimiter Prodanov. 2021. "The Active Segmentation Platform for Microscopic Image Classification and Segmentation" Brain Sciences 11, no. 12: 1645. https://doi.org/10.3390/brainsci11121645

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop