A Survey of Medical Image Classification Techniques

Abstract

Medical informatics is the field that combines two medical data sources: biomedical record and imaging data. Medical image data is formed by pixels that correspond to a part of a physical object and produced by imaging modalities. Exploration of medical image data methods is a challenge in the sense of getting their insight value, analyzing and diagnosing of a specific disease. Image classification plays an important role in computer-aided-diagnosis and is a big challenge on image analysis tasks. This challenge related to the use of methods and techniques in exploiting image processing result, pattern recognition result and classification methods and subsequently validating the image classification result into medical expert knowledge. The main objective of medical images classification is not only to reach high accuracy but also to identify which parts of human body are infected by the disease. This paper reviewed the state-of-the-art of image classification techniques to diagnose human body disease. The review covered identification of medical image classification techniques, image modalities used, the dataset and trade off for each technique. At the end, the review showed the improvement of image classification techniques such as to increase accuracy and sensitivity value, and to be feasible employed for computer-aided-diagnosis are a big challenge and an open research.

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A Survey of Medical Image Classification
Techniques
Eka Miranda, Mediana Aryuni
School of Information Systems
Bina Nusantara University
Jakarta, Indonesia
ekamiranda@binus.ac.id, mediana.aryuni@binus.ac.id
E. Irwansyah
School of Computer Science
Bina Nusantara University
Jakarta, Indonesia
eirwansyah@binus.edu
Abstract— Medical informatics is the study that combines two
medical data sources: biomedical record and imaging data.
Medical image data is formed by pixels that correspond to a part
of a physical object and produced by imaging modalities.
Exploration of medical image data methods is a challenge in the
sense of getting their insight value, analyzing and diagnosing of a
specific disease. Image classification plays an important role in
computer-aided-diagnosis and is a big challenge on image
analysis tasks. This challenge related to the usage of methods and
techniques in exploiting image processing result, pattern
recognition result and classification methods and subsequently
validating the image classification result into medical expert
knowledge. The main objective of medical images classification is
not only to reach high accuracy but also to identify which parts of
human body are infected by the disease. This paper reviewed the
state-of-the-art of image classification techniques to diagnose
human body disease. The review covered identification of
medical image classification techniques, image modalities used,
the dataset and trade off for each technique. At the end, the
reviews showed the improvement of image classification
techniques such as to increase accuracy and sensitivity value and
to be feasible employed for computer-aided-diagnosis are a big
challenge and an open research.
Keywords—medical informatics; image classification; disease
diagnosis
I.
I
NTRODUCTION
Medical Informatics is the study that intersects Information
Technology and healthcare [1]. In the implementation of field
research, medical informatics combines two medical data
sources. They are the biomedical record and imaging data,
which have specific characteristic [2]. Digital image data is
formed by pixels that correspond to a part of a physical object
as a result of imaging modalities [2]. In contrast biomedical
record is formed by record from patient medical tests. The
difference characteristic between biomedical record and
imaging data makes difference methodologies and techniques
needed to explore them. Researchers and methods to explore
biomedical record have already exploited [3, 4].
Medical image data is produced by imaging modalities. The
issue of this field is how to extract the image and classify the
extraction result into the similar pattern then identify and
understand which parts of human body are affected by the
specific disease from image classification result [5].
There are three stages of Medical Image analysis tasks
include (1) feature extraction and representation, (2) feature
selection that will be used for classification, and (3) feature and
image classification [6]. Moreover, computer-aided-diagnosis
needs the important role of image classification. Furthermore
medical image classification has three main steps: pre-
processing, feature extraction and classification [7]. After pre-
processing step then it needs to extract features of interest part
from the image for further analysis. The purpose of the pattern
classification system is to map the input variables (such as
record data or image data) become the output variables (to
represent one specific class (with a disease or with no a disease
class) [6].
Image classification is a big challenge on image analysis
tasks, especially the selection of methods and techniques in
exploiting the result of image processing and pattern
recognition, classification methods, subsequently validating the
image classification result into medical expert knowledge [7].
The main objective of medical images classification is not only
to reach high accuracy but also to identify which parts of
human body are infected by the disease. [8]. In the future, an
automatic diagnosis technique with image data is needed to be
developed for better clinical care [7].
Since image classification research is still an open area and
big challenge to validate the image classification result into
medical expert knowledge, this paper focuses only on detailed
review of mining medical image classification technique with
the state-of-the-art that addressing this issue. This paper will
not review about feature extraction and feature selection. The
aim of the review is to give the reader wide-ranging review of
mining medical image classification techniques includes their
pros and cons.
II. I
MAGING
M
ODALITIES
The source of medical images data is generated from
Biomedical Devices, which use the imaging techniques like
Computed Tomography (CT), Magnetic Resonance Imaging
(MRI) and mammogram [9].
There are several medical imaging modalities that involve
ionizing radiation, nuclear medicine, magnetic resonance,
ultrasound, and optical methods as a modality media. Each
modality media has a special characteristic and differences
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response to human body structure and organs tissue [5]. There
are four imaging modalities [9]:
A. Projectional Imaging
X-rays are a form of electromagnetic radiation (EM), which
has a wavelength range between 0.1-10 nm. They are translated
into photons with energy levels, 12-125 keV. The x-ray
imaging projection used almost at the same time with the need
to use laboratory testing as a medical diagnostic tool. Image
formation process is divided into three main steps: Image pre-
read, Image main read, Image processing [9].
B. Computed Tomography (CT)
The conventional x-ray imaging projection sometimes fails
in achieving good results because of tiny differences in
attenuation (less than 5%). CT improves the subject contrast
using discrimination less than 1%. The application for cancer
screening such as lung and virtual colonoscopy often uses CT.
There are several variations of CT imaging, namely: Positron
emission tomography (PET) / CT, CT perfusion, CT
angiography, Dual source and dual energy CT [9].
C. Magnetic Resonance (MR)
A powerful magnetic field is used in Magnetic Resonance
Imaging method (MR) for the nuclear magnetization
alignment of hydrogen atoms in water molecules. MR became
the standard of cross-sectional imaging modalities that useful
to visualize soft tissues (such as muscle, brain), fat and bone
(especially marrow bone) [9].
D. Ultrasound Imaging
The high- sound waves with the frequency range from 1-
20 MHz that can be applied to produce cross-sectional images
of the human body. The strength of the echo ultrasound return
depends on the characteristics of biological tissue which they
pass through.
III. M
EDICAL
I
MAGE
A
NALYSIS
T
ASKS
An image is considered as a representation of an object
with specific properties that are employed in image processing.
The medical image analysis tasks consist of Feature extraction
and representation, Feature selection that will be used for
classification, and Feature and image classification.
A. Feature Extraction and Representation
Features are an important measurement for image
understanding, especially the feature representation of the
segmented region that used for object classification and
analysis [10]. The techniques for Feature extraction and
representation include:
• Statistical Pixel-Level (SPL) Features
These features provide quantitative information about
the pixels within a segmented region. The SPL
features include: mean, variance, and a histogram of
the gray values of pixels in the region, and
additionally: the area of the region and information
about the contrast of pixels within the region and
edge gradient of boundary pixels [10].
• Shape Feature
These features provide information about shape
characteristic of the region boundary, include
circularity, compactness, moments, chain-codes, and
Hough transform. Morphological processing methods
have also been used for shape description [10].
• Texture Features
These features provide information about the local
texture within the region or related area of the image,
which are calculated using the second-order
statistical histogram or co-occurrence matrices.
Besides wavelet processing method for spatial-
frequency analysis is employed for local texture
information representation [10].
• Relational Features
These features provide information about the
relational and hierarchical structure of the regions
related to a single object or a group of objects [10].
B. Feature Selection for Classification
Feature selection used to discover the important features
that most appropriate for the classification task. Selection of
correlated features for dimensionality reduction in the
classification task can improve the computational efficiency
and classification performance. The final set of features can be
determined through data correlation, clustering, and analysis
algorithms to explore similarity patterns in the training data
[10]. A typical classification system showed in Fig.1.
Fig. 1. A classification system [6]
Feature selection for classification techniques are:
• Linear Discriminant Analysis (LDA)
The purpose of LDA method is to discovery a linear
combination of features which able to give the best
possible separation among different classes of data in
the feature space. It can reduce dimensionality space
for classification and also give better classification
accuracy [10].
• Principal Component Analysis (PCA)
It is an efficient method of dimensionality reduction
of a data set with a big number of interrelated
variables. However, for data with sparse distribution
and noise, PCA method may not provide optimal
selection of features [10].
• GA (Genetic Algorithms)-Based Optimization
It is a robust technique for search optimization that
uses natural selection principles. It utilizes prior
information and using selection for survival, and is
able to adapt to the specific parameter issues. The
parameters are encoded as binary strings that are
associated with the fitness measurement [10].
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TABLE I. C
OMPILATION OF MEDICAL IMAGE CLASSIFICATION TECHNIQUES
Author Name Year Methodolog
y
Pros and Cons
Method Imaging Modalities
A. A. A. Setio et al
[11]
2016 Multi-View Convolutional
Networks (ConvNets)
Pulmonary CT False positive reduction.
The CAD sensitivity performance should be enhanced.
D. Mittal and A. Rani
[12]
2016 SVM Ultrasound image High accuracy.
Each classifier being trained on only two out of N classes.
G. Van Tulder and M.
De Bruijne [13]
2016 Convolutional classification
Restricted Boltzmann Machine
(RBM).
Lung CT High mean classification accuracy.
Suitable for smaller representations learning with smaller
filters or hidden nodes.
J. Hong et al [14]
2016 Principal Nested Spheres
(PNS), Distance Weighted
Discrimination(DWD)
MRI AUC > 0.600.
Apply PNS separately.
K. Seetharaman and S.
Sathiamoorthy [15]
2016 Adaptive Binary T
r
ee Based
Support Vector Machine
(ABTSVM)
CT, MRI, Microscopy,
Mammogram,
Ultrasound, X-ray and
Endoscopy images
Low computational and storage cost.
The relevance judgments are performed using the ground
truth and the subjectivity of the individual user.
K. Sirinukunwattana et
al [16]
2016 Neighboring Ensemble
Predictor (NEP) +
Convolutional Neural
Network (CNN)
Histopathology images Accurately predict.
The Weighted Average F1 score and Multiclass AUC
result not considerably different with softmax CNN +
SSPP.
M. Anthimopoulos et
al [17]
2016 Convolutional
Neural Network (CNN)
Lung CT Scan
Drawback
High classification performance.
The training time becomes slower due to very large
number of parameters.
M. J. J. P. Van
Grinsven et al [18]
2016 Convolutional neural
networks (CNNs) + Selective
Sampling (SeS)
Color fundus image
High performance.
Uses the reference guide from a single expert.
Q. Dou et al [19]
2016 3D Convolutional Neural
Network
(CNN)
Cerebral micro-
b
leeds
(CMBs) MRI
High sensitivity 93:16%.
The accuracy and detection speed are not balance.
A. Masood and A. Al-
jumaily [20]
2015 SVM Biopsy samples High accuracy.
The error rate of classification decreased about 16.5% for
Histopathological and 6% for Dermoscopic images.
F. Khalvati, A. Wong,
and M. A. Haider [21]
2015 SVM classifier.
Multi-parametric
magnetic resonance
imaging (MP-MRI)
High sensitivity and specificity (>80%).
A limited number of datasets and the target of Gleason
score is >= 7, the proposed model was not assessed by
clinicians.
K. Chung et al [22]
2015 Pre-Trained Convolutional
Neural Networks (CNN)
CT scan AUC = 0.868.
Time-consuming since Peri-Fissural Nodules (PFN)
characterization was subjective, it suggests the increment
of the number of 2D views may give the higher accuracy
of characterization.
V. Gopalakrishnan, P.
G. Menon, and S.
Madan [23]
2015 Bayesian rule
learning (BRL) methods
Cardiovascular
Magnetic Resonance
Imaging (cMRI)
High accuracy.
A limited number of datasets.
Y. Iwahori et al [24] 2015
K
-means++ Endoscope
The accuracy is higher because using the edge-
b
ased
features.
The computational time was decreased if HOG features
used to detect the polyp region.
Y. Song et al [25]
2015 Locality-constrained Sub-
cluster Representation
Ensemble (LSRE)
High Resolution
Computed
Tomography (HRCT)
High accuracy.
The Locality-constrained Linear Coding (LLC) did not
use advanced distance function.
B. Manju, K.
Meenakshy, and R.
Gopikakumari [26]
2014 KNN classifie
r
CT images High accuracy.
A limited number of datasets.
G. N. Balaji, T. S.
Subashini, and N.
Chidambaram [27]
2014 BPNN, SVM Echocardiogram High performance (87.5%).
Exclude other views include subcostal and Doppler view.
L. Ai, X. Gao, and J.
Xiong [28]
2014 Mean-Shift Clustering
(MSC)
Functional magnetic
resonance imaging
(fMRI)
Low false positive rate.
The significance levels could not be easily theoretically
measured. Still, it may show some challenges when it
needs very accurate comparisons.
S. Yazdani, R. Yusof,
A. Riazi, and A.
Karimian [29]
2014 SVM Magnetic Resonance
Images (MRI)
Desirable performance.
Not consider sub-cortical structures and 3 T images.
Reduce error rate from 30% down to less than 10%. .
L. Tan et al [30] 2013 SVM classifiers for sMRI,
SVM classifiers and
Hierarchical clustering for
fMRI.
The structural MR
images (sMRI) and
functional MR images
(fMRI)
High accuracy.
Not include tissue density maps and functional
connectivity networks. fMRI classifier had difficulty in
classifying some of the negative subjects.
R. To
m
ari et al [31]
2014 ANN Light microscope that
equipped with
DinoEye Eyepiece
High accuracy.
A limited number of datasets.
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Camera
A. Andrade et al [32]
2012 Compare ANN, SVM and
kNN classifier.
Ultrasound
Images
Higher accuracy for SVM classifier.
A limited number of datasets.
J. Miguel, P. Dias, C.
Manta, A. Luís, and S.
Cruz [33]
2012 Feed-Forward
Backpropagation Neural
Network
Digital Retinal Images
High sensitivity and specificity.
The classification is not significantly affected by the
randomized initialization of the neural networks weights.
M. A. Dabbah et al
[34]
2011 The multi-scale
dual-model using the NNT
pixel classification
Corneal confocal
microscopy (CCM)
image
Maximum sensitivity and specificity is the EER of
15.44%.
Compared to manual analysis delivers equivalent results.
E. J. Ciaccio et al [35] 2010 Classification method (using
threshold
or incremental learning)
Capsule endoscopy
video clip images
High threshold classifier and high incremental classifier
(high sensitivity and specificity).
H. Wang and B. Fei
[36]
2009 Multiscale fuzzy C-means
(MsFCM) classification
method
MR images Overlap ratio is greater than 90% and valida
t
ed by the
ground truth.
Classification method was not sensitive to the threshold
b
etween 0.8 and 0.9.
M. Mete, L. Hennings,
H. J. Spencer, and U.
Topaloglu [37]
2009 Support Vecto
r
Machines
Regular digital camera
which is attached to a
microscope
Achieving 0.90 for overall f-measure.
Only measures set-based dimensional fraction component.
Sub-image has the same opportunities to be categorized as
the positive or negative value.
R. Marée, P. Geurts,
and L. Wehenkel [38]
2007 Random sub windows and
extremely randomized trees
b
iology cell image The accuracy is good enough without a specific pre-
processing neither the domain knowledge.
The issue is misclassification error rates
M. Niemeijer, M. D.
Abra, and B. Van
Ginneken [39]
2006 Suppor
t
Vector Machine
Retinopathy screening The ROC curve is 0.9968 and the accuracy is 0.974.
The attained accuracy of 0.974 is still can be improved.
S. P. Awate, T.
Tasdizen, N. Foster,
and R. T. Whitaker
[40]
2006 Markov statistics non-
parametrically
Magnetic Resonance
(MR) images
Higher mean (by a couple of percents) and lower standard
deviation
Only use one algorithm "nearest neighbors" in Markov
neighborhood technique
C. Feature and Image Classification
The selected features of image representation that are
generated from feature selection, are used in object
recognition and characterization. In the medical imaging
analysis, features and measurements can also be used for
region segmentation to extract meaningful structures,
subsequently, interpret the result using knowledge-based
model and classification methods [6]. Feature and image
classification techniques namely:
• Statistical Classification Methods
The categories of these methods are an unsupervised
and supervised approach. The unsupervised methods
cluster the data based on their separation in the feature
space, include K-means and fuzzy clustering. On the
other hand, a supervised approach needs training data,
test data, and class label to classify the data, include
probabilistic methods like the nearest neighbor and
Bayesian classifier [6].
• Rule-Based Systems
The system analyzes the feature vector using multiple
sets of rules that are designed to test specific
conditions in the feature vector database to set off an
action. The rules consist of two parts: condition
premises and actions, which are generated based on an
expert knowledge to deduce the action when the
conditions are satisfied. The action which part of the
rule could change the database state or label of a
feature vector based on a specific state of analysis.
Usually, a rule-based system consists of three sets of
rules: supervisory or strategy rules, a focus of
attention rules, and knowledge rules. The supervisory
or strategy rules control the analysis process and
provide the control actions include starting and
stopping action. The strategy rules determine which
rules would be tested during the analysis process. The
focus-of-attention rules provide specific features
within analysis process by accessing and extracting
the information or features from the database.
Subsequently, the rules convey the information from
the input (database) into the activity center where the
implementations of knowledge rules are scheduled.
Finally, the knowledge rules analyze the information
related to the required conditions then execute an
action that changes the output database [6].
• Neural Network Classifiers
Artificial neural network paradigms for feature
classification, object recognition and image
interpretation namely back-propagation, radial basis
function, associative memories, and self-organizing
feature maps. At that time fuzzy system-based
approaches have been applied in artificial neural
networks for better classification and generalization
result [6].
• Support Vector Machine (SVM) for Classification
The Relevance Vector Machine (RVM) combines
regression, classification, and a Bayesian probabilistic
principle. The other models for pattern classification
utilizing theoretical approaches include kernel-based
classifier and linear programming perturbation-based
methods [6].
Compilation of medical image classification techniques
showed in TABLE I.
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IV. D
ISCUSSION
There are several challenges for the computer to
understand an image. First: diversity and number of image
data increase continuously, Second: mathematical and
statistical formulation and techniques, and how they could be
adjusted in the medical domain, Third: computing power [2].
Subsequently, the challenge of medical image analysis
model development comes from: (1) Complexity of the image
data: Although the quality of an image generated by medical
imaging modalities could be improved, the information
captured by medical imaging modalities not always complete
and clear. This problem due to lack of technology and imaging
process which produce incompetent resolution and artifacts
[2], (2) Complexity of the model or prototype: Medical
imaging analysis involves various human body structures,
features on the different application. The objects of medical
imaging analysis could be atomic structures (e.g., spine, brain
cortex, coronary arteries), pathological tissue (e.g., tumor,
myocardial infarction, inflammation, edema), functional areas
(e.g., motor cortex, glucose metabolism), or artificial objects
(e.g., implants, electrodes, catheter tips) which show important
biological variability of a subject. Medical imaging model or
prototype explains the object based on previous knowledge
that must identify diverse human body visualization [2], (3)
Validation: A common problem in medical imaging analysis is
data validation [2]. Human measurements are not absolutely
accurate. Medical imaging algorithm requires a ground truth
standard to validate their result [41-42].
TABLE II shows classification scheme of medical image
classification for each image classification technique. Neural
network classifier and SVM are the most used technique for
image classification and they could classify image from
almost all image modalities, additionally, many researchers
that used this technique showed high accuracy and sensitivity,
reasonable prediction and high classification performance
result [12, 16-21, 26-27, 29-34]. In addition, high sensitivity
and the specificity value of the research should be completed
with a suitable number of datasets with the purpose of the
feasibility to be employed for computer-aided-diagnosis.
Furthermore, many medical image analysis researchers used
image data from CT, MR and ultrasound imaging modalities
[11-15,17,19,21-23,25,26,28-30,32,36,40], since these three
imaging modalities could be used to determine the presence or
absence of the lesion based on a patient history [9].
Additionally, Magnetic Resonance Imaging does not use x-
rays [9]. For the future work, the neural network plays an
important role in classification since it can be used with its
supervised and unsupervised techniques [16]. Additionally,
fuzzy system-based approaches have been applied in the
artificial neural network for better classification result [6, 36].
Another interesting challenge is reducing an error rate by
applying hybrid approach since applying single approach still
give a high error rate result (above 10%) [20, 29, 30, 37].
TABLE II. C
LASSIFICATION
S
CHEME OF MEDICAL IMAGE
CLASSIFICATION TECHNIQUE
Image Classification Techniques Imaging Modalities
Statistical Classification Methods X-Ray CT MR Ultrasound
V. Gopalakrishnan, P. G. Menon, √
and S. Madan [23]
Y. Iwahori et al [24] √
H. Wang and B. Fei [36] √
S. P. Awate, T. Tasdizen, N. Foster,
and R. T. Whitaker [40]
√
Neural Network Classifiers
M. Anthimopoulos et al [17] √
M. J. J. P. Van Grinsven et al [18] √
Q. Dou et al [19] √
Support Vector Machine (SVM)
D. Mittal and A. Rani [12] √
A. Masood and A. Al-jumaily [20] √
F. Khalvati, A. Wong, and M. A.
Haider [21]
√
K. Chung et al [22] √
B. Manju, K. Meenakshy, and R.
Gopikakumari [26]
√
S. Yazdani, R. Yusof, A. Riazi, and
A. Karimian [29]
√
M. Niemeijer, M. D. Abra, and B.
Van Ginneken [39]
√
V. CONCLUSION
AND
FUTURE
WORK
Medical image classification is an interesting research area,
it combines the diagnosis problem and analysis purposes in the
medical field. This paper has provided the detailed review of
image classification techniques for diagnosis of human body
disease include imaging modalities used, each dataset and pros
and cons for each technique. For the future work, the
improvement of image classification techniques will increase
accuracy value and subsequently feasible to be employed for
computer-aided-diagnosis, and more robust methods are being
developed.
A
CKNOWLEDGMENT
We gratefully acknowledge the supporting from institutions
that support this paper. Thank you to Research and Technology
Transfer office (RTTO), Bina Nusantara University which
helped the internal funding for this paper.
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