The rapid increase in the incidence of Alzheimer’s disease (AD) has become a critical issue in low and middle income
countries. In general, MR imaging has become sufficiently suitable in clinical situations, while CT scan might be
uncommonly used in the diagnosis of AD due to its low contrast between brain tissues. However, in those countries, CT
scan, which is less costly and readily available, will be desired to become useful for the diagnosis of AD. For CT scan,
the enlargement of the temporal horn of the lateral ventricle (THLV) is one of few findings for the diagnosis of AD. In
this paper, we present an automated volumetry of THLV with segmentation based on Bayes’ rule on CT images. In our
method, first, all CT data sets are normalized into an atlas by using linear affine transformation and non-linear wrapping
techniques. Next, a probability map of THLV is constructed in the normalized data. Then, THLV regions are extracted
based on Bayes’ rule. Finally, the volume of the THLV is evaluated. This scheme was applied to CT scans from 20 AD
patients and 20 controls to evaluate the performance of the method for detecting AD. The estimated THLV volume was
markedly increased in the AD group compared with the controls (P < .0001), and the area under the receiver operating
characteristic curve (AUC) was 0.921. Therefore, this computerized method may have the potential to accurately detect
AD on CT images.
The early diagnosis of idiopathic normal pressure hydrocephalus (iNPH) considered as a treatable dementia is important. The iNPH causes enlargement of lateral ventricles (LVs). The degree of the enlargement of the LVs on CT or MR images is evaluated by using a diagnostic imaging criterion, Evans index. Evans index is defined as the ratio of the maximal width of frontal horns (FH) of the LVs to the maximal width of the inner skull (IS). Evans index is the most commonly used parameter for the evaluation of ventricular enlargement. However, manual measurement of Evans index is a time-consuming process. In this study, we present an automated method to compute Evans index on brain CT images. The algorithm of the method consisted of five major steps: standardization of CT data to an atlas, extraction of FH and IS regions, the search for the outmost points of bilateral FH regions, determination of the maximal widths of both the FH and the IS, and calculation of Evans index. The standardization to the atlas was performed by using linear affine transformation and non-linear wrapping techniques. The FH regions were segmented by using a three dimensional region growing technique. This scheme was applied to CT scans from 44 subjects, including 13 iNPH patients. The average difference in Evans index between the proposed method and manual measurement was 0.01 (1.6%), and the correlation coefficient of these data for the Evans index was 0.98. Therefore, this computerized method may have the potential to accurately compute Evans index for the diagnosis of iNPH on CT images.
In this paper, we investigate the effect of the use of wavelet transform for image processing on radiation dose reduction
in computed radiography (CR), by measuring various physical characteristics of the wavelet-transformed images.
Moreover, we propose a wavelet-based method for offering a possibility to reduce radiation dose while maintaining a
clinically acceptable image quality. The proposed method integrates the advantages of a previously proposed technique,
i.e., sigmoid-type transfer curve for wavelet coefficient weighting adjustment technique, as well as a wavelet soft-thresholding
technique. The former can improve contrast and spatial resolution of CR images, the latter is able to
improve the performance of image noise. In the investigation of physical characteristics, modulation transfer function,
noise power spectrum, and contrast-to-noise ratio of CR images processed by the proposed method and other different
methods were measured and compared. Furthermore, visual evaluation was performed using Scheffe's pair comparison
method. Experimental results showed that the proposed method could improve overall image quality as compared to
other methods. Our visual evaluation showed that an approximately 40% reduction in exposure dose might be achieved
in hip joint radiography by using the proposed method.
KEYWORDS: Image quality, Signal to noise ratio, Modulation transfer functions, Imaging systems, Medical imaging, Digital mammography, Radiography, Image resolution, Spatial frequencies, X-rays
We describe an information-theoretic method for quantifying overall image quality in terms of mutual information (MI). MI is used to express the amount of information that an output image contains about an input object. The more the MI value provides, the better the image quality is. Therefore, the overall quality of an image can be quantitatively evaluated by measuring MI. We demonstrated by way of image simulation that MI increases with increasing contrast and decreases with the increase of noise and blur. We investigated the utility of this method by applying it to evaluate the performance of four imaging plate detectors. We also compared evaluation results in terms of MI against those in terms of the detective quantum efficiency conventionally used for characterizing the efficiency performance of imaging systems. Our results demonstrate that the proposed method is simple to implement and has potential usefulness for evaluation of overall image quality.
This paper presents an information-entropy based metric for combined evaluation of resolution and noise properties of
radiological images. The metric is expressed by the amount of transmitted information (TI). It is a measure of how much
information that one image contains about an object or an input. Merits of the proposed method are its simplicity of
computation and the experimented setup. A computer-simulated step wedge was used for simulation study on the
relationship of TI and the degree of blur as well as the noise. Three acrylic step wedges were also manufactured and used
as test sample objects for experiments. Two imaging plates for computed radiography were employed as information
detectors to record X-ray intensities. We investigated the effects of noise and resolution degradation on the amount of TI
by varying exposure levels. Simulation and experimental results show that the TI value varies when the noise level or the
degree of blur is changed. To validate the reasoning and usefulness of the proposed metric, we also calculated and
compared the modulation transfer functions and noise power spectra for the employed imaging plates. Results show that
the TI has close correlation with both image noise and image blurring, and it may offer the potential to become a simple
and generally applicable measure for quality evaluation of medical images.
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