65,976 research outputs found
Computing contrast ratio in medical images using local content information
Rationale
Image quality assessment in medical applications is often based on quantifying the visibility between a structure
of interest such as a vessel, termed foreground (F) and its surrounding anatomical background (B), i.e., the
contrast ratio. A high quality image is the one that is able to make diagnostically relevant details distinguishable
from the background. Therefore, the computation of contrast ratio is an important task in automatic medical
image quality assessment.
Methods
We estimate the contrast ratio by using Weber’s law in local image patches. A small image patch can contain a
flat area, a textured area or an edge. Regions with edges are characterized by bimodal histograms representing B
and F, and the local contrast ratio can be estimated using the ratio between mean intensity values of each mode
of the histogram. B and F are identified by computing the mid-value between the modes using the ISODATA
algorithm. This process is performed over the entire image with a sliding window resulting in a contrast ratio per
pixel.
Results
We have tested our measure on two general purpose databases (TID2013 [1] and CSIQ [2]) to demonstrate that
the proposed measure agrees with human preferences of quality. Since our measure is specifically designed for
measuring contrast, only images exhibiting contrast changes are used. The difference between the maximum of
the contrast ratios corresponding to the reference and processed images is used as a quality predictor. Human
quality scores and our proposed measure are compared with the Pearson correlation coefficient. Our
experimental results show that our method is able to accurately predict changes of perceived quality due to
contrast decrements (Pearson correlations higher than 90%). Additionally, this method can detect changes in
contrast level in interventional x-ray images acquired with varying dose [3]. For instance, the resulting contrast
maps demonstrate reduced contrast ratios for vessel edges on X-ray images acquired at lower dose settings, i.e.,
lower distinguishability from the background, compared to higher dose acquisitions.
Conclusions
We propose a measure to compute contrast ratio by using Weber’s law in local image patches. While the
proposed contrast ratio is computationally simple, this approximation of local content has shown to be useful in
measuring quality differences due to contrast decrements in images. Especially, changes in structures of interest
due to low contrast ratio can be detected by using the contrast map making our method potentially useful in Xray
imaging dose control.
References
[1] Ponomarenko N. et al., “A New Color Image Database TID2013: Innovations and Results,” Proceedings of
ACIVS, 402-413 (2013).
[2] Larson E. and Chandler D., "Most apparent distortion: full-reference image quality assessment and the role of
strategy," Journal of Electronic Imaging, 19 (1), 2010.
[3] Kumcu, A. et al., “Interventional x-ray image quality measure based on a psychovisual detectability model,”
MIPS XVI, Ghent, Belgium, 2015
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
Enhancing retinal images by nonlinear registration
Being able to image the human retina in high resolution opens a new era in
many important fields, such as pharmacological research for retinal diseases,
researches in human cognition, nervous system, metabolism and blood stream, to
name a few. In this paper, we propose to share the knowledge acquired in the
fields of optics and imaging in solar astrophysics in order to improve the
retinal imaging at very high spatial resolution in the perspective to perform a
medical diagnosis. The main purpose would be to assist health care
practitioners by enhancing retinal images and detect abnormal features. We
apply a nonlinear registration method using local correlation tracking to
increase the field of view and follow structure evolutions using correlation
techniques borrowed from solar astronomy technique expertise. Another purpose
is to define the tracer of movements after analyzing local correlations to
follow the proper motions of an image from one moment to another, such as
changes in optical flows that would be of high interest in a medical diagnosis.Comment: 21 pages, 7 figures, submitted to Optics Communication
No-reference image quality assessment through the von Mises distribution
An innovative way of calculating the von Mises distribution (VMD) of image
entropy is introduced in this paper. The VMD's concentration parameter and some
fitness parameter that will be later defined, have been analyzed in the
experimental part for determining their suitability as a image quality
assessment measure in some particular distortions such as Gaussian blur or
additive Gaussian noise. To achieve such measure, the local R\'{e}nyi entropy
is calculated in four equally spaced orientations and used to determine the
parameters of the von Mises distribution of the image entropy. Considering
contextual images, experimental results after applying this model show that the
best-in-focus noise-free images are associated with the highest values for the
von Mises distribution concentration parameter and the highest approximation of
image data to the von Mises distribution model. Our defined von Misses fitness
parameter experimentally appears also as a suitable no-reference image quality
assessment indicator for no-contextual images.Comment: 29 pages, 11 figure
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