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J. Imaging
Special Issues
AI Approaches to Biological Image Analysis
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AI Approaches to Biological Image Analysis

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 October 2018)

Special Issue Editors

Dr. Andrew French

E-Mail Website
Guest Editor
1. Computer Vision Laboratory, School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, UK
2. School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
Interests: bioimage analysis; plant phenotyping; deep learning
Dr. Michael Pound

E-Mail Website
Guest Editor
Computer Vision Laboratory, School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, UK
Interests: network snakes; object tracking; level sets; stereo reconstruction; event detection

Special Issue Information

Dear Colleagues,

Recent machine learning and AI (artificial intelligence)-based approaches have had remarkable impact on the image analysis field, and we can expect such successes to spread to specific disciplines as the techniques are applied to specific domains. In this Special Issue, we will present some recent advances and applications within the field of bioimage analysis. We are particularly interested in exploring application of machine and deep learning approaches to the analysis of biological images (excluding medical images). One particular area where this has seen recent application is plant and crop phenotyping, but we expect to see advances in phenotyping success across the discipline.

We welcome submissions in this area, including, but not limited to:

  • Novel application of deep or machine learning to bioimaging problems
  • Application of such approaches to improve plant and crop phenotyping
  • Novel application within the wider biological imaging field, including microscope imaging, hyperspectral imaging, and 3D/4D imaging.

Dr. Andrew French
Dr. Michael Pound
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioimage analysis
  • Phenotyping
  • Deep learning
  • Machine learning

Published Papers (2 papers)

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Research

21 pages, 6274 KiB  
Article
Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
by Pouria Sadeghi-Tehran, Plamen Angelov, Nicolas Virlet and Malcolm J. Hawkesford
J. Imaging 2019, 5(3), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging5030033 - 01 Mar 2019
Cited by 18 | Viewed by 7302
Abstract
Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated [...] Read more.
Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identification and high-resolution remote sensing data to evaluate the accuracy and the computational efficiency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures. Full article
(This article belongs to the Special Issue AI Approaches to Biological Image Analysis)
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1 pages, 682 KiB  
Article
Transfer Learning from Synthetic Data Applied to Soil–Root Segmentation in X-Ray Tomography Images
by Clément Douarre, Richard Schielein, Carole Frindel, Stefan Gerth and David Rousseau
J. Imaging 2018, 4(5), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging4050065 - 06 May 2018
Cited by 45 | Viewed by 7825
Abstract
One of the most challenging computer vision problems in the plant sciences is the segmentation of roots and soil in X-ray tomography. So far, this has been addressed using classical image analysis methods. In this paper, we address this soil–root segmentation problem in [...] Read more.
One of the most challenging computer vision problems in the plant sciences is the segmentation of roots and soil in X-ray tomography. So far, this has been addressed using classical image analysis methods. In this paper, we address this soil–root segmentation problem in X-ray tomography using a variant of supervised deep learning-based classification called transfer learning where the learning stage is based on simulated data. The robustness of this technique, tested for the first time with this plant science problem, is established using soil–roots with very low contrast in X-ray tomography. We also demonstrate the possibility of efficiently segmenting the root from the soil while learning using purely synthetic soil and roots. Full article
(This article belongs to the Special Issue AI Approaches to Biological Image Analysis)
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