The authors developed a simple method for determining the presampling modulation transfer function (MTF). which includes the unsharpness of the detector and the effect of the sampling aperture, in digital radiographic (DR) systems. With this method, the presampling MTF is determined by the Fourier transform of a ;finely sampled' line spread function (LSF) obtained with a slightly angulated slit in a single exposure. Since the effective sampling distance becomes much smaller than the original sampling distance of the DR system, the effect of aliasing on the MTF calculations can be eliminated. The authors applied this method to the measurement of the presampling MTF of a compound radiographic system and examined the directional dependence, the effect of exponential extrapolation, and the effect of different sampling distances. It is shown that the technique of multiple slit exposure and exponential extrapolation of the LSF tail, which has been commonly used in analog seven-film systems, can be employed in DR systems. The authors determined the glare fraction in order to estimate the component of low-frequency drop mainly due to ;glare'
This paper presents a simple and straightforward method for synthetically evaluating digital radiographic images by a single parameter in terms of transmitted information (TI). The features of our proposed method are (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image noise and resolution (blur). Two acrylic step wedges with 0-1-2-3-4-5 and 0-2-4-6-8-10 mm in thickness were used as phantoms for experiments. In the present study, three experiments were conducted. First, to investigate the relation between the value of TI and image noise, various radiation doses by changing exposure time were employed. Second, we examined the relation between the value of TI and image blurring by shifting the phantoms away from the center of the X-ray beam area toward the cathode end when imaging was performed. Third, we analyzed the combined effect of deteriorated blur and noise on the images by employing three smoothing filters. Experimental results show that the amount of TI is closely related to both image noise and image blurring. The results demonstrate the usefulness of our method for evaluation of physical image quality in medical imaging.
In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.
The hyperdense middle cerebral artery (MCA) dot sign representing a thromboembolus is one of the important computed tomography (CT) findings for acute stroke on unenhanced CT images. Our purpose in this study was to develop an automated method for detection of the MCA dot sign of acute stroke on unenhanced CT images. The algorithm of the method which we developed consisted of 5 major steps: extraction of the sylvian fissure region, initial identification of MCA dots based on the morphologic top-hat transformation, feature extraction of candidates, elimination of false positives (FPs) by use of a rule-based scheme, and classification of candidates using a support vector machine (SVM) classifier with four features. Our database comprised 297 CT images obtained from seven patients with the MCA dot sign. The performance of this scheme for classification of the MCA dot sign was evaluated by means of a leave-one-case out method. The performance of the classification by use of the SVM achieved a maximum sensitivity of 97.5% (39/40) at a FP rate of 1.28 per image. The sensitivity for detection of the MCA dot sign was 97.5% (39/40) with a FP rate of 0.5 per hemisphere. The method we developed has the potential to detect the MCA dot sign of acute stroke on unenhanced CT images.
Purpose: Detection of early CT signs of infarct in nonenhanced CT image is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early CT sign of acute cerebral infarction, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the principal early CT sign is subtle hypoattenuation. An image processing method to reduce local noise with edges preserved was developed to improve infarct detection. Rationale: An adaptive partial median filter (APMF) was selected for this application, since the APMF can markedly improve the visibility of the normal gray-white matter interface. APMF should enhance the conspicuity of gray-white matter interface changes due to hypoattenuation that accompanies cerebral infarction. Method: In a criterion referenced performance study using simulated CT images with gray-white matter interfaces, a total of 14 conventional smoothing filters were also used for comparison to validate the usefulness of the proposed APMF. The APMF indicated the highest performance among the compared methods. Then, observer performance study by receiver operator characteristic (ROC) analysis was performed with 4 radiologist observers using a database with 18 abnormal and 33 normalhead CT images. Results:The average A z values of ROC curves for all radiologists increased from 0.876 without the APMF images to 0.926 with the APMF images, and this difference was statistically significant (P = 0.04). Conclusions:The results from the two observer performance studies demonstrated that APMF has significant potential to improve the diagnosis of acute cerebral infarction using nonenhanced CT images.
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