Computer Vision in Healthcare Applications

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Editorial
Computer Vision in Healthcare Applications
Junfeng Gao ,
1,2,3
Yong Yang ,
4
Pan Lin,
1,2,3
and Dong Sun Park
5
1
College of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China
2
Key Laboratory of Cognitive Science, State Ethnic Affairs Commission, Wuhan 430074, China
3
Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan 430074, China
4
School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China
5
IT Convergence Research Center, Chonbuk National University, Jeonju, Jeonbuk 54896, Republic of Korea
Correspondence should be addressed to Yong Yang; yangyong5080@126.com
Received 27 December 2017; Accepted 28 December 2017; Published 4 March 2018
Copyright © 2018 Junfeng Gao et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The research of computer vision, imaging processing and
pattern recognition has made substantial progress during
the past several decades. Also, medical imaging has
attracted increasing attention in recent years due to its
vital component in healthcare applications. Investigators
have published a wealth of basic science and data docu-
menting the progress and healthcare application on medical
imaging. Since the development of these research fields has
set the clinicians to advance from the bench to the bedside,
the Journal of Healthcare Engineering set out to publish this
special issue devoted to the topic of advanced computer
vision methods for healthcare engineering, as well as review
articles that will stimulate the continuing efforts to
understand the problems usually encountered in this field.
The result is a collection of fifteen outstanding articles
submitted by investigators.
Following the goal of special issue, we identify four major
domains covered by the papers. The first is medical image
analysis for healthcare, the second is the computer vision
for predictive analytics and therapy, the third is fundamental
algorithms for medical images, and the last one focuses on
the machine learning algorithms for medical images. Here,
we give the review of these published papers.
1. Analysis of Medical Image
This theme attempts to address the improvement and new
techniques on the analysis methods of medical image. First,
integration of multimodal information carried out from
different diagnostic imaging techniques is essential for a
comprehensive characterization of the region under exam-
ination. Therefore, image coregistration has become crucial
both for qualitative visual assessment and for quantitative
multiparametric analysis in research applications. S. Monti
et al. in Italy “An Evaluation of the Benefits of Simulta-
neous Acquisition on PET/MR Coregistration in Head/
Neck Imaging”compare and assess the performance
between the traditional coregistration methods applied to
PET and MR acquired as single modalities and the obtained
results with the implicitly coregistration of a hybrid PET/
MR, in complex anatomical regions such as the head/neck
(HN). The experimental results show that hybrid PET/MR
provides a higher registration accuracy than the retrospec-
tively coregistered images.
The feature extraction is one of the key issues for the
analysis of medical images. I. I. Esener et al. in Turkey
“A New Feature Ensemble with a Multistage Classification
Scheme for Breast Cancer Diagnosis”develop a new and
effective feature ensemble with a multistage classification
which is used in a computer-aided diagnosis (CAD) sys-
tem for breast cancer diagnosis. In this new method, four
features, the local configuration pattern-based, statistical,
and frequency domain features were concatenated as feature
vectors, and eight well-known classifiers are used in a mul-
tistage classification scheme. High classification accuracy
was obtained, and it shows that the proposed multistage
classification scheme is more effective than the single-
stage classification for breast cancer diagnosis.
Hindawi
Journal of Healthcare Engineering
Volume 2018, Article ID 5157020, 4 pages
https://doi.org/10.1155/2018/5157020

Currently, the traditional approach to reduce colorectal
cancer-related mortality is to perform regular screening in
search for polyps, which results in polyp miss rate and
inability to perform visual assessment of polyp malig-
nancy. D. Vazquez et al. in Spain and Canada “A Benchmark
for Endoluminal Scene Segmentation of Colonoscopy
Images”propose an extended benchmark of colonoscopy
image segmentation and establish a new strong benchmark
for colonoscopy image analysis. By training a standard fully
convolutional networks (FCN), they show that in endolum-
inal scene segmentation, the performance of FCN is better
than the result of the prior researches.
2. Computer Vision for Predictive Analytics
and Therapy
Computer vision technique has shown great application
in surgery and therapy of some diseases. Recently, three-
dimensional (3D) modeling and rapid prototyping tech-
nologies have driven the development of medical imaging
modalities, such as CT and MRI. P. Gargiulo et al. in
Iceland “New Directions in 3D Medical Modeling: 3D-
Printing Anatomy and Functions in Neurosurgical Planning”
combine CT and MRI images with DTI tractography and
use image segmentation protocols to 3D model the skull
base, tumor, and five eloquent fiber tracts. The authors
provide a great potential therapy approach for advanced
neurosurgical preparation.
The elderly is easy to fall and it will harm the body and
accordingly has serious negative mental impacts on them.
T.-H. Lin et al. in Taiwan “Fall Prevention Shoes Using
Camera-Based Line-Laser Obstacle Detection System”design
an interesting line-laser obstacle detection system to prevent
the elderly from falls. In the system, a laser line passes
through a horizontal plane and has a specific height to the
ground, and optical axis in a camera has a specific inclined
angle to the plane, and hence, the camera can observe the
laser pattern to obtain the potential obstacles. Unfortunately,
this system designed is useful mainly for indoor applications
instead of outdoor environment.
Human activity recognition (HAR) is one of the
widely studied computer vision problem. S. Zhang et al.
in China “A Review on Human Activity Recognition
Using Vision-Based Method”introduce an overview of
various HAR approaches as well as their evolutions with
the representative classical literatures. The authors highlight
the advances of image representation approaches and
classification methods in vision-based activity recognition.
Representation approaches generally include global repre-
sentations, local representations, and depth-based represen-
tations. They accordingly divide and describe the human
activities into three levels including action primitives,
actions/activities, and interactions. Also, they summarize
the classification techniques in HAR application which
include 7 types of method from the classic DTW and the
newest deep learning. Lastly, they address that applying these
current HAR approaches in real-world systems or applica-
tions has great challenge although up to now recent HAR
approaches have achieved great success. Also, three future
directions are recommended in their work.
3. Fundamental Algorithms for Medical Images
The majority of this issue focuses on the research of
improved algorithm for medical images. Organ segmentation
is a prerequisite for CAD systems. In fact, the segmentation
algorithm is the most important and basic for image process-
ing and also enhance the level of disease prediction and ther-
apy. C. Pan et al. in China “Leukocyte Image Segmentation
Using Novel Saliency Detection Based on Positive Feedback
of Visual Perception”use the ensemble of polyharmonic
extreme learning machine (EPELM) and positive feedback
of perception to detect salient objects, which is totally data-
driven without any prior knowledge and labeled samples
compared with the existed algorithms. A positive feedback
module based on EPELM focuses on fixation area for the
purpose of intensifying objects, inhibiting noises, and pro-
moting saturation in perception. Experiments on several
standard image databases show that the novel algorithm
outperforms the conventional saliency detection algorithms
and also segments nucleated cells successfully in different
imaging conditions.
High-intensity focused ultrasound (HIFU) has been
proposed for the safe ablation of both malignant and
benign tissues and as an agent for drug delivery, while
MRI has been proposed for guidance and monitoring for
the therapy. A. Vargas-Olivares et al. in México and Canada
“Segmentation Method for Magnetic Resonance-guided
High-Intensity Focused Ultrasound Therapy Planning”used
the MR images for the HIFU therapy planning and propose
an efficient segmentation approach. The segmentation
scheme uses the watershed method to identify the regions
found on the HIFU treatment. In addition, the authors also
propose a thread pool strategy, in order to reduce the compu-
tational overload of the processing time of the group of MR
images and the segmentation algorithm.
Recently, random walkers (RW) have attracted a growing
interest to process segmentation of medical images. How-
ever, classical RW method needs a long computation time
and a high memory usage because of the construction of
corresponding large-scale graph to solve the resulting sparse
linear system. C. Dong et al. in China and USA “An
Improved Random Walker with Bayes Model For Volu-
metric Medical Image Segmentation”incorporate the prior
(shape and intensity) knowledge in the optimization of
sparse linear system. Integrating the Bayes model into the
RW sparse system, the organ is automatically segmented
for the adjacent slice, which is called RWBayes algorithm in
the article. Compared with the conventional RW and the
state-of-the-art interactive segmentation methods, their
method can significantly improve the segmentation accuracy
and could be extended to segment other organs in the future.
Automatic segmentation of the spinal cord in MR images
remains a difficult task. C.-C. Liao et al. in Taiwan “Atlas-
Free Cervical Spinal Cord Segmentation on Midsagittal
T2-Weighted Magnetic Resonance Images”present an auto-
matic segmentation method on sagittal T2-weighted images.
2 Journal of Healthcare Engineering

The method is atlas-free, in which expectation maximization
algorithm is used to cluster the pixels on a midsagittal MR
image according to their gray levels or SIs. Dynamic pro-
gramming is used to detect anatomical structures and their
edges. The detection of the anterior and posterior edges of
the spinal cord within the cervical spinal canal is finally
successful in all 79 images, showing its high accuracy and
robustness. Based on this proposed algorithm, using alone
or combining with others, one can develop a computer-
aided diagnosis system with massive screening on cervical
spine diseases. Finally, the authors point out several limita-
tions in the algorithm, such as its inability to be applied to
lower lumbar spinal levels.
The misalignments originated from motion and defor-
mation often result in errors in estimating an apparent dif-
fusion coefficient (ADC) map fitted with prostate DWI, and
the ADC map is an important indicator in diagnosing pros-
tate cancer. Until now, there are few studies that focus on
this misalignment in prostate DWI. L. Hao et al. in China
“Nonrigid Registration of Prostate Diffusion-Weighted
MR”apply affine transformation to DWI to correct intraslice
motions. Then, nonrigid registration based on free-form
deformation (FFD) is used to compensate for intraimage
deformations. The experimental results show that the pro-
posed algorithm can correct the misalignment of prostate
DWI and decrease the artifacts of ROI in the ADC maps.
These ADC maps thus obtain sharper contours of lesions,
which are helpful for improving the diagnosis and clinical
staging of prostate cancer.
Medical ultrasound is widely used in the diagnosis and
assessment of internal body structures and also plays a key
role in treating various diseases due to its safety, noninvasion,
and well tolerance in patients. However, the images are
always contaminated with speckle noise and hence hinder
the identification of image details. Currently, many methods
have been proposed to remove the noise and preserve the
image details at the same time. M. Szczepański and K. Radlak
in Poland “Digital Path Approach Despeckle Filter for Ultra-
sound Imaging and Video”propose a so-called escaping
paths based on traditional digital paths, and also, they
extend this concept from the spatial domain (2D) to the
spatiotemporal domain (3D) that is designed for multipli-
cative noise suppression, specifically for ultrasound image
and video filtering. In addition, the extended neighbor-
hood model is used to increase the filter denoising ability,
which is based on von Neumann concept derived from
cellular automata theory. The experimental results prove
that the proposed removal technique outperforms the
state-of-the-art approach for multiplicative noise removal
with lower computational overload which enables one to
complete image processing tasks and image enhancement
of video streams in a real-time environment.
A primary challenge in accelerating MR imaging is how
to reconstruct high-resolution images from undersampled
k-space data. There is a trade-offbetween the spatial resolu-
tion and temporal resolution. J. Chen et al. in China “Low-
Rank and Sparse Decomposition Model for Accelerating
Dynamic MRI Reconstruction”introduce a low-rank and
sparse decomposition model to resolve this problem, which
is based on the theory of robust principal component
analysis (RPCA). Unlike k-t RPCA (a method that uses
the low-rank plus sparse decomposition prior to reconstruc-
tion of dynamic MRI from part of the k-space measure-
ments), the authors propose inexact augmented Lagrangian
method (IALM) to solve the optimization of RPCA and
to accelerate the dynamic MRI reconstruction from highly
undersampled k-space data, which has a generalized for-
mulation capability of separating dynamic MR data into
low-rank and sparse component. The experimental results
on cardiac datasets prove that the proposed method can
achieve more satisfactory reconstruction performance and
faster reconstruction speed, compared with the state-of
the-art reconstruction methods.
4. Machine Learning Algorithms for
Medical Images
The growth of the older adult population in the world is
surprising and it will have a great impact on the healthcare
system. The elders always lack self-care ability and hence,
healthcare and nursing robot draw much attention in recent
years. Although somatosensory technology has been intro-
duced into the activity recognition and healthcare interaction
of the elderly, traditional detection method is always in a
single modal. In order to develop an efficient and convenient
interaction assistant system for nurses and patients with
dementia, X. Dang et al. in China “An Interactive Care
System Based on a Depth Image and EEG for Aged Patients
with Dementia”propose two novel multimodal sparse auto-
encoder frameworks based on motion and mental features.
First, the motion is extracted after the preprocessing of depth
image and then EEG signals as the mental feature is recorded.
The proposed novel system is designed to be based on the
multimodal deep neural networks for the patient with
dementia with special needs. The input features of the
networks include (1) extracted motion features based on
the depth image sensor and (2) EEG features. The output
layer is the type recognition of the patient’s help requirement.
Experimental results show that the proposed algorithm
simplifies the process of the recognition and achieved
96.5% and 96.4% (accuracy and recall rate), respectively, for
the shuffled dataset, and 90.9% and 92.6%, respectively, for
the continuous dataset. Also, the proposed algorithms sim-
plify the acquisition and data processing under high action
recognition ratio compared with the traditional method.
Recently, deep learning has become very popular in
artificial intelligence. Q. Song et al. in China “Using Deep
Learning for Classification of Lung Nodules on Computed
Tomography Images”employ a convolution neural network
(CNN), deep neural network (DNN), and stacked autoenco-
der (SAE) for the early diagnosis of lung cancer to doctors.
The experimental results suggest that the CNN archived the
best performance than DNN and SAE.
N. D. Kamarudin et al. in Malaysia and Japan “A
Fast SVM-Based Tongue’s Colour Classification Aided by
k-Means Clustering Identifiers and Colour Attributes as
Computer-Assisted Tool for Tongue Diagnosis”propose a
two-stage classification system for tongue color diagnosis
3Journal of Healthcare Engineering

aided with the devised clustering identifiers, and it can
diagnose three tongue colors: red, light red, and deep red.
The diagnosis system is very useful for the early detection
of imbalance condition inside the body. Experimental result
shows that this novel classification system outperforms the
conventional SVM by 20% computational time and 15% in
terms of classification accuracy.
5. Conclusion
These authors highlight both the promise and the challenges
faced by this healthcare application field of medical images.
Their researches identify the critical need for clinical and
theory prospective of medical images. This special issue
brings about various new developments in computer vision
about medical images and clinical application. In summary,
this special issue provides a snapshot of the computer vision
in healthcare applications on medical images across the
globe. Hopefully, this publication will provide a good refer-
ence for future computer vision, analysis algorithms, and
machine learning of medical images. However, there are still
some key messages that emerge from the papers compiled
within this special issue: there still remain limitation and
challenge for computer vision and various algorithms and
processing techniques of medical images although these
works show good efficiency than traditional and state-of-art
methods. We hope that this theme issue will further advance
our understanding of computer vision about medical image
processing and healthcare applications and pave the way
for new directions in medical images and computer vision
research across health and disease.
Acknowledgments
This work was supported by the National Nature Science
Foundation of China (61773408, 81271659, 61662026, and
61473221). The guest editors are very thankful to all the
anonymous reviewers of the journal and the perseverant
and generous support of the editor in chief.
Junfeng Gao
Yong Yang
Pan Lin
Dong Sun Park
4 Journal of Healthcare Engineering