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INTERNATIONAL JOURNAL OF DESIGN, ANALYSIS AND TOOLS FOR INTERGRATED CIRCUITS AND SYS TEMS, VOL. 1, NO. 1, NOVEMBER 2009
Data Analysis of Medical Images
Yuhao Sun, Gabriela Mogos
Abstract— Medical imaging techniques are increasingly and
widely used in the medical community, especially hospitals and
healthcare institutions. The doctors, including clinicians and
radiologists, can view the internal construction of the particular
organ or tissue of patients so that they can confirm the
corresponding diagnosis and treatment suggestions to patients.
The medical images with the distortion can affect the decisions
of doctors at the clinical level. The paper presents a study on the
impact of visual distortion on medical images.
Index Terms— Medical imaging, Image Quality Assessment,
Subjective IQA, Objective IQA, No-Reference IQA
I. INTRODUCTION
Medical imaging techniques are widely adopted in the
medical community worldwide in order to assist doctors to give
precise judgments to patients [1]. Typical medical imaging
techniques include X-ray Computed Tomography (CT),
Magnetic Resonance Imaging (MRI), Ultrasound Imaging and
other types [2]. Currently, medical imaging techniques still
have several flaws, in which image distortion is one of them.
Visual distortion on medical images generally is demonstrated
as blurry, contrast-distorted and noise-distorted [1]. However,
the impact of visual distortion on medical images is not usually
positive and cannot be easily ignored; the images with
distortion potentially influence the judgments from doctors,
which possibly cause the results of misdiagnosis, missed-
diagnosis, or other inaccurate judgements [2]. Thus, it is
significant to find a new and efficient mathematical model to
find the possibly distorted images and alert doctors in advance.
Currently, many good mathematical models of Image Quality
Assessment have been proved as efficient when detecting the
distortions of images, including Blind/Referenceless Image
Spatial Quality Evaluator (BRISQUE) [3], Naturalness Image
Quality Evaluator (NIQE) [4], Perception based Image Quality
Evaluator (PIQE) [5] and others [6-10]. However, some of them
are lack of the proof that they can work still well under medical
imaging circumstances.
This paper proposes a methodology to perceive the impact of
visual distortion on medical images. Specifically, we have
proved which mathematical model can work precisely to
determine potential distorted medical images, by using two
significant Image Quality Assessments, i.e., Subjective Image
Quality Assessment (Subjective IQA) and Objective Image
Quality Assessment (Objective IQA) [1].
Manuscript received .
All authors are with the Department of Computer Science and Software
Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China.
(emails:yuhao.sun20@imperial.ac.uk, Gabriela.Mogos@xjtlu.edu.cn).
II. METHODOLOGY
A suitable methodology has been designed to achieve goals
smoothly. The used methodology contains three main steps:
Dataset Construction, Subjective IQA and Objective IQA.
Firstly, the project dataset has been constructed, i.e., medical
images dataset. A project-tailored and high-quality dataset is
the key to result in positive outcomes. The dataset aims to
consist of 50 medical images of CT scans, in which 20 images
of them are deemed as distortion-free and provided by a highly
reputed hospital or healthcare institution. Other images are
processed to distorted status computationally by 20 images
aforementioned accessed from the hospital, by using MATLAB
purely. Possible distorted status can be blurry, contrast- and
noise-distorted. According to the requirements of image files,
image quality, currency and image annotations, we adopted the
images in a dataset named DeepLesion, provided by NIH
(National Institutes of Health) [12]. Samples of medical images
in DeepLesion can be referred to Fig 1.
In order to simulate the reality to the maximum extent, more
than 50 doctors have been invited to complete a questionnaire
so that we can precisely allocate the number of distorted images
to different distorted types as real as possible. In the
questionnaire, doctors have to select one or more possible
distorted types which may happen during their working time.
The eventual statistics show “Blurry” happened most in the
reality of medical imaging (76% respondents chose); “Contrast-
distorted” is the second frequent distorted type (43% chose); the
third one is “Obscured-distorted” (31% chose); “Compression”
is the least distorted circumstance (16% chose). Additionally,
12% of respondents have chosen to make a note on other types
of distortion, including image artifacts and other human-
intervention occasions.
Eventually, three significant types of distortion, which are
“Blurry”, “Contrast-distorted” and “Obscured-distorted”, have
been selected. The number of distorted images to each type is
based on the statistics aforementioned – they are 76%, 43% and
31% respectively. Alternatively, “Obscured-distorted” has been
changed to “Noise-distorted” as it will be tough to simulate the
obscuration on the images by mere computers. Therefore, 30
distorted images can be calculated as the percentage ratio (after
suitable adjustment and round up or down) – 76%: 43%: 31%
≈ 17: 9: 4. Finally, we built the project dataset successfully,
which consists of 20 good quality (regarded as distortion-free)
CT scans, 17 blurry CT scans, 9 contrast-distorted CT scans,
and 4 noise distorted CT scans. Sample of a group of processed
images can be referred to Fig 2 in the Appendix.
INTERNATIONAL JOURNAL OF DESIGN, ANALYSIS AND TOOLS FOR INTERGRATED CIRCUITS AND SYSTEMS, VOL. 1, NO. 1, NOVEMBER 2009
The second step of the whole methodology is to conduct
Subjective IQA. Subjective IQA is one of two significant IQA
approaches and regarded as the most trustworthy approach to
decide the quality of an image, as the respondents of Subjective
IQA are purely human beings – same to the reality. In
Subjective IQA of our project, we have selected three doctors
from a local highly reputed hospital, including two clinicians
and one radiologist. At least two of three doctors have more
than thirteen-year clinical experience in their specific
professions. All three doctors have been invited to give scores
to all medical images in the dataset we have constructed earlier
(50 CT scans), according to their own experiences only and
without any other references. During the whole process of
Subjective IQA, the possible influential factors (IFs) which
might cause negative results to the project, including System IF,
Context IF and Human IF, have been well considered. To
minimize the possible adverse effects, all respondents have
completed the Subjective IQA at the same time and in the most
suitable place where was remarkably similar to their daily
working places. For example, the clinicians have conducted
Subjective IQA in departmental clinics, and the radiologist has
conducted Subjective IQA in the office of the department of
radiology, with a high-quality display. Additionally, “Daily
Emotional Self-Declaration” has been provided for doctors to
answer, in order to ensure their responses were not negatively
affected by their potential negative emotions.
Three doctors are deemed as professionals in their fields, i.e.,
clinical departments and radiology. The doctors have given a
score to each medical image, based on a 5-point Likert scale. If
the score of the image is closer to five, the quality of the image
is better and vice versa. All doctors have been asked to
complete the Subjective IQA within the given time, 50 mins –
even though it has been found that their average completed time
is only around to 30 mins. However, to set a fixed time period,
i.e. 50 mins, is one of the requirements to conduct a Subjective
IQA.
The third and final step of the whole methodology is to
conduct Objective IQA. Objective IQA is another essential IQA
approach. In Objective IQA, various mathematical models are
core, and they can generate numerical score results. Generally,
there are three types of Objective IQA which are Full-Reference
IQA (FR-IQA), Reduced-Reference IQA (RR-IQA), and No-
Reference IQA (NR-IQA), according to the availability of
reference images [11]. Equivalent to the approach as the same
as how medical images are usually assessed in hospitals, NR-
IQA should be considered in our case, which means there is no
reference image involved. Three mathematical models of NR-
IQA, including Blind/Referenceless Image Spatial Quality
Evaluator (BRISQUE) [3], Naturalness Image Quality
Evaluator (NIQE) [4], Perception based Image Quality
Evaluator (PIQE) [5], will be adopted. With the toolbox
provided by MATLAB, our Objective IQA will be conducted
wholly within MATLAB.
Different from Subjective IQA, it is not too sure if the
generated results of Objective IQA are within the expectations
or not. To ensure the results of Objective IQA are reasonable
and logical, conducting “Initial Check” to every mathematical
model used is essential. In each model’s Initial Check, images
have been checked by groups – the qualities of distortion-free
images should be better than those are distorted. For example,
an acceptable group of scores possibly can be 40 (a good-
quality image), 50 (a blurry-distorted image), 60 (a contrast-
distorted image) – the fewer scores stand for the better qualities,
and this principle works for all three models.
III. RESULTS
The results of Subjective IQA and Objective IQA are
numerically, but with different range and meaning. In
Subjective IQA, the results are in range 1 and 5 in integral form;
the larger score stands for, the better quality. In Objective IQA,
the results are in decimal form and range 0 to 100; the lower
score stands for, the better quality, this is following the rules of
NR-IQA mathematical models.
As mentioned earlier, for each assessment, initial check
firstly conducted to confirm the rationality of results in case of
any errors, outliers and some accidental circumstances. A valid
result by initial checks shows that the quality of distortion-free
images is better than those distorted images numerically.
In Subjective IQA, two clinicians and one radiologist have
made scores to every single medical image.
In Objective IQA, BRISQUE and NIQE have been first
conducted because of their influences among IQA researches.
After the initial check to BRISQUE and NIQE, only 30% and
28% of results have matched our expectations. Due to
unsatisfactory results obtained with BRISQUE and NIQE, it is
mandatory to add another mathematical model, PIQE. After the
initial check to the performance of PIQE, 82% of results of
PIQE match our expectations. Eventually, the results generated
by PIQE are fully considered as the final results of Objective
IQA and all data will be analyzed in the following chapter.
IV. COMPARATIVE ANALYSIS
According to the numerical results of Subjective IQA and
Objective IQA above generated, comparative analysis can
provide further information in detail. The analysis results have
been divided into three categories according to the functions,
which are the main results, clinical results and computational
results. Main results are towards to project aims and objectives.
In clinical results, some ideas from clinical perspectives have
been raised, i.e., hospital or healthcare institutions relevant. In
computational results, several novel findings in computer
science discipline have been discussed. Following contents will
demonstrate three above categories respectively.
For the main results, with comparison, the results of
Subjective IQA and Objective IQA are highly similar, up to 80%
similarity. Additionally, the mathematical model named
Perception based Image Quality Evaluator (PIQE) has
INTERNATIONAL JOURNAL OF DESIGN, ANALYSIS AND TOOLS FOR INTERGRATED CIRCUITS AND SYSTEMS, VOL. 1, NO. 1, NOVEMBER 2009
performed well in the experiments on the condition of medical
images, especially X-ray CT scans.
For the clinical results, firstly, there were no significant
differences between the results from clinicians and radiologist.
They usually can have the same or similar decisions toward a
single case. Secondly, both doctors were sensitive to the
changes of contrast values of images. Thirdly, both doctors
were not highly sensitive to the changes of blurry values when
the standard deviation for Gaussian filter is within a specific
range. This is explainable that human eyes can accept slight
changes in images but perceiving the same information.
For the computational results, there are several novel
findings. Firstly, and most significantly, two famous NR-IQA
mathematical models, BRISQUE and NIQE have been proved
during experiments that they did not work well among X-ray
CT scans, which is out of our original expectations. In addition
to clinical results aforementioned, it has been proved that there
will be no adverse effects to doctors within the range (0.5, 1.0)
for Gaussian filter with a standard deviation when the image is
distorted by blurry. Within a range of grayscale for contrast
values, specifically (0.1, 0.3) for ‘low_in’ and (0.6, 0.8) for
‘high_in’, it possibly will improve the quality of images from
doctors’ perspective. All of the numerical factors involved
above are relevant to the functions in MATLAB toolboxes,
especially imgaussfilt() and imadjust().
V. CONCLUSIONS
Through a three-step methodology by Dataset Construction,
Subjective Image Quality Assessment and Objective Image
Quality Assessment, we surprisingly have found the
mathematical model, Perception based Image Quality
Evaluator (PIQE), can be considered as an efficient model when
working in medical imaging discipline, especially X-ray CT
scans. Additionally, the inadequacy of Blind/Referenceless
Image Spatial Quality Evaluator (BRISQUE) and Naturalness
Image Quality Evaluator (NIQE) in medical imaging discipline
has been proved during the research. Lastly, we have
summarized other minor results which have found during the
research from the clinical and computational perspectives,
respectively.
ACKNOWLEDGEMENT
We wish to thank the Research Institute of Big Data
Analytics (RIBDA), Xi’an Jiaotong-Liverpool University,
Suzhou, China, for supporting our contributions to this paper
through the RIBDA conference subsidy fund.
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INTERNATIONAL JOURNAL OF DESIGN, ANALYSIS AND TOOLS FOR INTERGRATED CIRCUITS AND SYSTEMS, VOL. 1, NO. 1, NOVEMBER 2009
Fig. 1. Sample Medical Images in DeepLesion (with annotation and without)
Fig. 2. Sample of a Group of Processed Images.
