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Stitching, Alignment and Segmentation Applications in Biomedical Images

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7386

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Special Issue Editors

Dr. Shuohong Wang

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Guest Editor

Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
Interests: computer vision; biomedical image analysis; machine learning

Dr. Francesco Fontanella

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Guest Editor

Department of Electrical and Information Engineering, University of Cassino and southern Lazio, 03043 Cassino, FR, Italy
Interests: evolutionary computation; machine learning; feature selection; pattern recognition; bayesian networks; cultural heritage
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Special Issue Information

Dear Colleagues,

Image stitching, alignment, and segmentation are common image processing steps in biomedical processing tasks, which are attracting more and more computer vision researchers’ attention. Although many conventional methods and machine-learning-based methods have been proposed to solve those problems, challenges still exist, e.g., 1) varied image quality (especially for some medical images); 2) large data size (may be terabyte to petabyte level); 3) high-precision requirement in biomedical scenarios. Thus, more advanced algorithms for image stitching, alignment, and segmentation are urgently needed.

Dr. Shuohong Wang
Dr. Francesco Fontanella
Guest Editors

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Keywords

computer vision
biomedical image analysis
image stitching
image alignment
image segmentation
machine learning

Published Papers (4 papers)

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Research

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12 pages, 3334 KiB

Open AccessArticle

Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

by Jonas Grande-Barreto, Eduardo Polanco-Castro, Hayde Peregrina-Barreto, Eduardo Rosas-Mialma and Carmina Puig-Mar

Information 2023, 14(7), 375; https://doi.org/10.3390/info14070375 - 1 Jul 2023

Cited by 1 | Viewed by 1237

Abstract

Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or templates, often unavailable due to privacy restrictions. Even when this baseline is available, the standard procedure combines the information to generate a single template as a starting point, reducing the variability in the generated synthetic images. This work proposes a methodology for building synthetic images of trabecular bone structure, creating a 3D network that simulates it. Next, the technical characteristics of the micro-CT scanner, the biomechanical properties of trabecular bones, and the physics of the imaging process to produce a synthetic image are simulated. The proposed methodology does not require biological samples, datasets, or templates to generate synthetic images. Since each synthetic image built is unique, the methodology is enabled to generate a vast number of synthetic images, useful in the performance comparison of algorithms under different imaging conditions. The created synthetic images were assessed using microarchitecture parameters of reference, and experimental results provided evidence that the obtained values match approaches requiring initial data. The scope of this methodology covers research aspects related to using synthetic images in further biomedical research or the development of educational training tools to understand the medical image. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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18 pages, 4639 KiB

Open AccessArticle

The Detection of COVID-19 in Chest X-rays Using Ensemble CNN Techniques

by Domantas Kuzinkovas and Sandhya Clement

Information 2023, 14(7), 370; https://doi.org/10.3390/info14070370 - 29 Jun 2023

Cited by 6 | Viewed by 1544

Abstract

Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses from chest X-rays and have extended this work to prove the feasibility of rapidly diagnosing COVID-19 with high degrees of accuracy. One issue in previous research has been the use of datasets containing only a few hundred images of chest X-rays containing COVID-19, causing CNNs to overfit the image data. This leads to lower accuracy when the model attempts to classify new images, as would be clinically expected. In this work, we present a model trained on the COVID-QU-Ex dataset containing 33,920 chest X-ray images, with an equal share of COVID-19, Non-COVID pneumonia, and Normal images. The model is an ensemble of pre-trained CNNs (ResNet50, VGG19, and VGG16) and GLCM textural features. The model achieved a 98.34% binary classification accuracy (COVID-19/no COVID-19) on a test dataset of 6581 chest X-rays and 94.68% for distinguishing between COVID-19, Non-COVID pneumonia, and normal chest X-rays. The results also demonstrate that a higher 98.82% three-class test accuracy can be achieved using the model if the training dataset only contains a few thousand images. However, the generalizability of the model suffers due to the smaller dataset size. This study highlights the benefits of both ensemble CNN techniques and larger dataset sizes for medical image classification performance. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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15 pages, 5113 KiB

Open AccessArticle

Virtual CT Myelography: A Patch-Based Machine Learning Model to Improve Intraspinal Soft Tissue Visualization on Unenhanced Dual-Energy Lumbar Spine CT

by Xuan V. Nguyen, Devi D. Nelakurti, Engin Dikici, Sema Candemir, Daniel J. Boulter and Luciano M. Prevedello

Information 2022, 13(9), 412; https://doi.org/10.3390/info13090412 - 31 Aug 2022

Viewed by 2232

Abstract

Background: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurately distinguish CSF from neural or other tissues based on the center voxel and neighboring voxels. Methods: 88 regions of interest (ROIs) from 12 patients’ dual-energy (100 and 140 kVp) lumbar spine CT exams were manually labeled by a neuroradiologist as one of 4 major tissue types (water, fat, bone, and nonspecific soft tissue). Four-class classifier convolutional neural networks were trained, validated, and tested on thousands of nonoverlapping patches extracted from 82 ROIs among 11 CT exams, with each patch representing pixel values (at low and high energies) of small, rectangular, 3D CT volumes. Different patch sizes were evaluated, ranging from 3 × 3 × 3 × 2 to 7 × 7 × 7 × 2. A final ensemble model incorporating all patch sizes was tested on patches extracted from six ROIs in a holdout patient. Results: Individual models showed overall test accuracies ranging from 99.8% for 3 × 3 × 3 × 2 patches (N = 19,423) to 98.1% for 7 × 7 × 7 × 2 patches (N = 1298). The final ensemble model showed 99.4% test classification accuracy, with sensitivities and specificities of 90% and 99.6%, respectively, for the water class and 98.6% and 100% for the soft tissue class. Conclusions: Convolutional neural networks utilizing local low-level features on dual-energy spine CT can yield accurate tissue classification and enhance the visualization of intraspinal neural tissue. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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Review

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28 pages, 1919 KiB

Open AccessReview

A Review of Imaging Methods and Recent Nanoparticles for Breast Cancer Diagnosis

by Fahimeh Aminolroayaei, Saghar Shahbazi-Gahrouei, Amir Khorasani and Daryoush Shahbazi-Gahrouei

Information 2024, 15(1), 10; https://doi.org/10.3390/info15010010 - 22 Dec 2023

Cited by 1 | Viewed by 1603

Abstract

Breast cancer is the foremost common cause of death in women, and its early diagnosis will help treat and increase patients’ survival. This review article aims to look at the studies on the recent findings of standard imaging techniques and their characteristics for breast cancer diagnosis as well as on the recent role of nanoparticles (NPs) that are used for breast cancer detection. Herein, a search was performed in the literature through scientific citation websites, including Google Scholar, PubMed, Scopus, and Web of Science, until May 2023. A comprehensive review of different imaging modalities and NPs for breast cancer diagnosis is given, and the successes, challenges, and limitations of these methods are discussed. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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