Computational and Mathematical Methods in Medicine

Machine Learning Applications in Medical Image Analysis


Status
Published

Lead Editor

1University of Louisville, Louisville, USA

2The University of Auckland, Auckland, New Zealand

3Illinois Institute of Technology, Chicago, USA


Machine Learning Applications in Medical Image Analysis

Description

Machine learning is one of the major tools of medical image analysis for today’s computer-aided diagnosis (CAD). Prior knowledge, learned from characteristic examples provided by medical experts, helps to guide image registration, fusion, segmentation, and other analyzing steps towards describing accurately the initial data and CAD goals and extracting reliable diagnostic cues. For example, quantitative 3D shape analyses of the corpus callosum on brain MRI help in diagnosing autism or dyslexia.

Although the CAD systems employ many promising and efficient learning techniques, recent neuroimaging advances in functional and structural magnetic resonance imaging (MRI), such as, for example, diffusion-weighted MRI and other modalities for visualizing brain and nervous system, call for both enhancing known traditional learning methods and applying new prospective ones (such as, e.g., deep learning of multilayer convolutional neural networks or high-order Markov random field models to predict states of a brain).

This issue focuses on both avenues of the use of machine learning in medical image analysis. Researchers are invited to contribute original research articles and / or reviews stimulating current permanent efforts to solve these important problems.

Potential topics include but are not limited to the following:

  • Thoracic imaging: developing CAD systems for lung images acquired from different modalities such as computed tomography (CT) and MRI. These CAD systems include lung segmentation, registration, and nodule segmentation detection.
  • Abdominal imaging: developing CAD systems for the diagnosis of abnormalities from different organs, such as the kidney, the liver, the colon, and the prostate. The images for such organs are acquired using a wide variety of imaging modalities such as CT, MRI, and ultrasound.
  • Brain imaging: developing CAD systems for the diagnosis of several abnormalities from images acquired form different modalities such as MRI. Such abnormalities include but are not limited to autism, dyslexia, epilepsy, and Parkinson’s disease.
  • Retinal imaging: developing CAD system for diagnosis of retinal abnormalities from optical coherence tomography (OCT) images. These CAD systems include the segmentation of multiple retinal layers, followed by detecting early subtle changes for the diagnosis of disorders such as diabetes and macular degeneration.

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 2361061
  • - Editorial

Machine Learning Applications in Medical Image Analysis

Ayman El-Baz | Georgy Gimel’farb | Kenji Suzuki
  • Special Issue
  • - Volume 2017
  • - Article ID 8064743
  • - Research Article

Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval

Liyan Chen | Beizhan Wang | ... | Yihan Ma
  • Special Issue
  • - Volume 2017
  • - Article ID 5271627
  • - Research Article

Topological Measurements of DWI Tractography for Alzheimer’s Disease Detection

Nicola Amoroso | Alfonso Monaco | ... | Alzheimer’s Disease Neuroimaging Initiative
  • Special Issue
  • - Volume 2017
  • - Article ID 9818506
  • - Research Article

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

Fahmi Khalifa | Ahmed Soliman | ... | Ayman El-Baz
  • Special Issue
  • - Volume 2017
  • - Article ID 5109530
  • - Research Article

Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel

Jianfeng Hu
  • Special Issue
  • - Volume 2017
  • - Article ID 2938504
  • - Research Article

Assessment of Iterative Closest Point Registration Accuracy for Different Phantom Surfaces Captured by an Optical 3D Sensor in Radiotherapy

Gerald Krell | Nazila Saeid Nezhad | ... | Günther Gademann
  • Special Issue
  • - Volume 2016
  • - Article ID 6740956
  • - Research Article

A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses

Mohammad I. Daoud | Tariq M. Bdair | ... | Rami Alazrai
  • Special Issue
  • - Volume 2016
  • - Article ID 6215085
  • - Research Article

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

Wei Li | Peng Cao | ... | Junbo Wang
  • Special Issue
  • - Volume 2016
  • - Article ID 1091279
  • - Research Article

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier

Keming Mao | Zhuofu Deng
  • Special Issue
  • - Volume 2016
  • - Article ID 2962047
  • - Research Article

Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction

Jinke Wang | Haoyan Guo
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate32%
Submission to final decision46 days
Acceptance to publication39 days
CiteScore3.400
Impact Factor1.770
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