Eri Matsuyama

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

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.

Improving Image Quality in Medical Images Using a Combined Method of Undecimated Wavelet Transform and Wavelet Coefficient Mapping

We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of denoised images obtained from the first component. This methodology can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. To confirm its superiority over existing state-of-the-art methods, the proposed method is experimentally evaluated via 30 mammograms and 20 chest radiographs. It is demonstrated that the proposed method can further improve the image quality of mammograms and chest radiographs, as compared to two other methods in the literature. These results reveal the effectiveness and superiority of the proposed method.

Mutual information-based evaluation of image quality with its preliminary application to assessment of medical imaging systems

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.

Comparison of a discrete wavelet transform method and a modified undecimated discrete wavelet transform method for denoising of mammograms

The purpose of this study was to evaluate the performance of a conventional discrete wavelet transform (DWT) method and a modified undecimated discrete wavelet transform (M-UDWT) method applied to mammographic image denoising. Mutual information, mean square error, and signal to noise ratio were used as image quality measures of images processed by the two methods. We examined the performance of the two methods with visual perceptual evaluation. A two-tailed F test was used to measure statistical significance. The difference between the M-UDWT processed images and the conventional DWT-method processed images was statistically significant (P<;0.01). The authors confirmed the superiority and effectiveness of the M-UDWT method. The results of this study suggest the M-UDWT method may provide better image quality as compared to the conventional DWT.

Physical characterization of digital radiological images by use of transmitted information metric

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.

Recent Advances of Quality Assessment for Medical Imaging Systems and Medical Images

This chapter describes the importance of medical image quality assessment and reviews major factors used for characterizing physical properties of medical images. Recent trends in assessment of medical imaging systems and medical images are also addressed. We will begin with a brief review of the concepts and definitions of some conventional medical image quality metrics and then describe a recently proposed image quality metric. We also provide two clinical applications related to quality assessment for medical images. The first application deals with the improvement of image quality in mammography. The second application addresses the effect of radiation dose reduction on image quality in digital radiography.

Radiation dose reduction in digital radiography using wavelet-based image processing methods

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 Scheffes 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.

Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network

Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.

Efficacy of a Combined Wavelet Shrinkage Method for Low-Dose and High-Quality Digital Radiography

The amount of radiation dose to the patient from digital radiography is of great concern. In particular, it is important to keep the radiation dose exposure to a minimum for patients in their reproductive period, who frequently undergo repeated radiation exposure during the course of diagnostic imaging and treatment follow-up. However, there is a well-recognized trade-off between image quality and radiation dose. The balancing of dose and image quality should be performed explicitly to ensure that patient doses are kept as low as reasonable achievable, while maintaining a clinically acceptable image quality. In response to this issue, many researchers have conducted extensive studies on developing image processing methods. In this paper, we propose an improved wavelet-transform-based method for offering a possibility to reduce the radiation dose while maintaining a clinically acceptable image quality. The proposed method integrates the advantages of our previously proposed wavelet-coefficient weighted method and the existing BayesShrink thresholding method. To verify the effectiveness of the proposed method, we measured and compared the presampled modulation transfer functions and the noise power spectra (NPS) of the processed computed radiography images. Furthermore, variations of contrast and NPS with respect to radiation dose were also examined. Visual evaluations were also performed by five experienced radiological technologists. Experimental results demonstrated that the proposed method could improve the resolution characteristic while keeping the noise level within an acceptable limit. Our visual evaluation showed that an approximately 40% reduction in exposure dose might be achieved with the proposed method in hip joint and lumbar spine radiographs.

A Method for Mammographic Image Denoising Based on Hierarchical Correlations of the Coefficients of Wavelet Transforms

In this work the authors present an effective denoising method to attempt to reduce 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 in the present study. Moreover, we conducted a perceptual evaluation of the processed images obtained from the proposed method and other conventional methods for confirmation of the effectiveness of the proposed approach. The experimental results show that our proposed method has the potential to effectively reduce noise while maintaining high-frequency information of original images.