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 a computerized scheme to assist MRI operators in accurate and rapid determination of sagittal
sections for MRI exam of cervical spinal cord. The algorithm of the proposed scheme consisted of 6 steps: (1) extraction
of a cervical vertebra containing spinal cord from an axial localizer image; (2) extraction of spinal cord with sagittal
image from the extracted vertebra; (3) selection of a series of coronal localizer images corresponding to various,
involved portions of the extracted spinal cord with sagittal image; (4) generation of a composite coronal-plane image
from the obtained coronal images; (5) extraction of spinal cord from the obtained composite image; (6) determination of
oblique sagittal sections from the detected location and gradient of the extracted spinal cord. Cervical spine images
obtained from 25 healthy volunteers were used for the study. A perceptual evaluation was performed by five experienced
MRI operators. Good agreement between the automated and manual determinations was achieved. By use of the
proposed scheme, average execution time was reduced from 39 seconds/case to 1 second/case. The results demonstrate
that the proposed scheme can assist MRI operators in performing cervical spinal cord MRI exam accurately and rapidly.
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.
Detection of early infarct signs on non-enhanced CT is mandatory in patients with acute ischemic stroke. We present a method for improving the detectability of early infarct signs of acute ischemic stroke. This approach is considered as the first step for computer-aided diagnosis in acute ischemic stroke. Obscuration of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early infarct sign, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the early infarct sign is subtle hypoattenuation. In order to improve the detectability of the early infarct sign, an image processing being able to reduce local noise with edges preserved is desirable. To cope with this issue, we devised an adaptive partial smoothing filter (APSF). Because the APSF can markedly improve the visibility of the normal gray-white matter interface, the detection of conspicuity of obscuration of gray-white matter interface due to hypoattenuation could be increased. The APSF is a specifically designed filter used to perform local smoothing using a variable filter size determined by the distribution of pixel values of edges in the region of interest. By adjusting four parameters of the APSF, an optimal condition for image enhancement can be obtained. In order to determine a major one of the parameters, preliminary simulation was performed by using composite images simulated the gray-white matter. The APSF based on preliminary simulation was applied to several clinical CT scans in hyperacute stroke patients. The results showed that the detectability of early infarct signs is much improved.
In this paper we describe a method for assisting radiological technologists in their routine work to automatically determine the imaging plane in lumbar MRI. The method is first to recognize the spinal cord and the intervertebral disk (ID) from the lumbar vertebra 3-plane localizer image, and then the imaging plane is automatically determined according to the recognition results. To determine the imaging plane, the spinal cord and the ID are automatically recognized from the lumbar vertebra 3-plane localizer image with a series of image processing techniques. The proposed method consists of three major steps. First, after removing the air and fat regions from the 3-plane localizer image by use of histogram analysis, the rachis region is specified with Sobel edge detection filter. Second, the spinal cord and the ID were respectively extracted from the specified rachis region making use of global thresholding and the line detection filter. Finally, the imaging plane is determined by finding the straight line between the spinal cord and the ID with the Hough transform. Image data of 10 healthy volunteers were used for investigation. To validate the usefulness of our proposed method, manual determination of the imaging plane was also conducted by five experienced radiological technologists. Our experimental results showed that the concordance rate between the manual setting and automatic determination reached to 90%. Moreover, a remarkable reduction in execution time for imaging-plane determination was also achieved.
In this paper we present a genetic-algorithm-based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme is applied to discriminate myocardial heart disease from echocardiographic images and to detect and classify clustered microcalcifications from mammograms. Unlike the conventional types of membership functions such as trapezoid, triangle, S curve, and singleton used in fuzzy reasoning, Gaussian-distributed fuzzy membership functions (GDMFs) are employed in the present study. The GDMFs are initially generated using various texture-based features obtained from reference images. Subsequently the shapes of GDMFs are optimized by a genetic-algorithm learning process. After optimization, the classifier is used for disease discrimination. The results of our experiments are very promising. We achieve an average accuracy of 96% for myocardial heart disease and accuracy of 88.5% at 100% sensitivity level for microcalcification on mammograms. The results demonstrated that our proposed genetic-algorithm-based fuzzy-logic approach is an effective method for computer-aided diagnosis in disease classification.
We previously developed a scheme to automatically detect pulmonary nodules on CT images, as a part of computer-aided diagnosis (CAD) system. The proposed method consisted of two template-matching approaches based on simple models that simulate real nodules. One was a new template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules within the lung area. The other one was a conventional template matching along the lung wall [lung wall template matching (LWTM)] for detecting nodules on the lung wall. After the two template matchings, thirteen feature values were calculated and used for eliminating false positives. Twenty clinical cases involving a total of 557 sectional images were applied; 71 nodules out of 98 were correctly detected with the number of false positives at approximately 30.8/case by applying two template matchings (GATM and LWTM) and elimination process of false positives. In this study, five features were newly added, and threshold-values of our previous features were reconsidered for further eliminating false positives. As the result, the number of false positives was decreased to 5.5/case without elimination of true positives.
The purpose of this study is to develop a computerized scheme for the discrimination between benign and malignant clustered microcalcifications that would aid radiologists in interpreting mammograms. In our scheme, microcalcifications in regions of interest (ROIs) are detected by using morphological filter. Then, four feature values including the total number, mean area, mean circularity and mean minimum distance of microcalcifications are calculated for classification. Gaussian-distributed membership functions used for fuzzy logic are determined from means and standard deviations of these feature values. Finally, fuzzy logic using the genetic-algorithm for optimization of membership functions is employed to classify clustered microcalcifications in unknown ROI. Our scheme was applied to twenty mammographic images with microcalcifications in the Mammographic Image Analysis Society database, containing thirteen benign and twelve malignant ROIs. Of the images ten each benign and malignant ROIs were used for training in fuzzy logic. The remaining five images were classified as benign or malignant cases by fuzzy logic. All sets of their combinations were employed to obtain the result. As the results, the average accuracy was approximately 88% (sensitivity: 100%, specificity: 77%), and Az value of ROC curve was 0.95.
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