Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017

© 2019 Background and Objective: Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970–2017). Methods: The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. Results: CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. Conclusions: It can be concluded that CMPB is a world-renowned publication outlet for biomedical researchers which has been growing in a number of publications since 1970. The analysis also conclude that the journal is very international with publications from all over the world although today European countries are the most productive ones

Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

Microfocal X-Ray Computed Tomography Post-Processing Operations for Optimizing Reconstruction Volumes of Stented Arteries During 3D Computational Fluid Dynamics Modeling

Restenosis caused by neointimal hyperplasia (NH) remains an important clinical problem after stent implantation. Restenosis varies with stent geometry, and idealized computational fluid dynamics (CFD) models have indicated that geometric properties of the implanted stent may differentially influence NH. However, 3D studies capturing the in vivo flow domain within stented vessels have not been conducted at a resolution sufficient to detect subtle alterations in vascular geometry caused by the stent and the subsequent temporal development of NH. We present the details and limitations of a series of post-processing operations used in conjunction with microfocal X-ray CT imaging and reconstruction to generate geometrically accurate flow domains within the localized region of a stent several weeks after implantation. Microfocal X-ray CT reconstruction volumes were subjected to an automated program to perform arterial thresholding, spatial orientation, and surface smoothing of stented and unstented rabbit iliac arteries several weeks after antegrade implantation. A transfer function was obtained for the current post-processing methodology containing reconstructed 16 mm stents implanted into rabbit iliac arteries for up to 21 days after implantation and resolved at circumferential and axial resolutions of 32 and 50 μm, respectively. The results indicate that the techniques presented are sufficient to resolve distributions of WSS with 80% accuracy in segments containing 16 surface perturbations over a 16 mm stented region. These methods will be used to test the hypothesis that reductions in normalized wall shear stress (WSS) and increases in the spatial disparity of WSS immediately after stent implantation may spatially correlate with the temporal development of NH within the stented region

Software Corrections of Vocal Disorders

We discuss how vocal disorders can be post-corrected via a simple nonlinear noise reduction scheme. This work is motivated by the need of a better understanding of voice dysfunctions. This would entail a twofold advantage for affected patients: Physicians can perform better surgical interventions and on the other hand researchers can try to build up devices that can help to improve voice quality, i.e. in a phone conversation, avoiding any surgigal treatment. As a first step, a proper signal classification is performed, through the idea of geometric signal separation in a feature space. Then through the analysis of the different regions populated by the samples coming from healthy people and from patients affected by T1A glottis cancer, one is able to understand which kind of interventions are necessary in order to correct the illness, i.e. to move the corresponding feature vector from the sick region to the healthy one. We discuss such a filter and show its performance.Comment: Computer Methods and Programs in Biomedicine, accepted for publicatio

Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Computer Methods and Programs in Biomedicine XXX (2013) XXX‐XXX 1 Effective Management of Medical Information through ROI-Lossless Fragile Image Watermarking Technique

In this article, we have proposed a blind, fragile and Region of Interest (ROI) lossless medical image watermarking (MIW) technique, providing an all-in-one solution tool to various medical data distribution and management issues like security, content authentication, safe archiving, controlled access retrieval and captioning etc. The proposed scheme combines lossless data compression and encryption technique to embed electronic health record (EHR)/DICOM metadata, image hash, indexing keyword, doctor identification code and tamper localization information in the medical images. Extensive experiments (both subjective and objective) were carried out to evaluate performance of the proposed MIW technique. The findings offer suggestive evidence that the proposed MIW scheme is an effective all-in-one solution tool to various issues of medical information management domain. Moreover, given its relative simplicity, the proposed scheme can be applied to the medical images to serve in many medical applications concerned with privacy protection, safety, and management etc. Keywords

Detection of Hard Exudates in Retinal Fundus Images using Deep Learning

Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an early stage. Research has been done to detect hard exudates using regular image processing techniques and Machine Learning techniques. Here, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems, Computation, Automation and Networking http://icscan.in