Tzong-Jer Chen

A novel image quality index using Moran I statistics

Measurement of image quality is very important for various applications such as image compression, restoration and enhancement. Conventional methods (e.g., mean squared error; MSE) use error summation to measure quality change pixel by pixel and do not correlate well with subjective quality measurement. This is due to the fact that human eyes extract structural information from the viewing field. In this study a new quality index using a Moran I statistics is proposed. The Moran statistic that measures the sharpness from a local area is a good index of quality as most image processing techniques alter the smoothness of the image. Preliminary results show that the new quality index outperforms the MSE significantly under various types of image distortions.

Quality of compressed medical images.

Previous studies have shown that Joint Photographic Experts Group (JPEG) 2000 compression is better than JPEG at higher compression ratio levels. However, some findings revealed that this is not valid at lower levels. In this study, the qualities of compressed medical images in these ratio areas (∼20), including computed radiography, computed tomography head and body, mammographic, and magnetic resonance T1 and T2 images, were estimated using both a pixel-based (peak signal to noise ratio) and two 8 × 8 window-based [Q index and Moran peak ratio (MPR)] metrics. To diminish the effects of blocking artifacts from JPEG, jump windows were used in both window-based metrics. Comparing the image quality indices between jump and sliding windows, the results showed that blocking artifacts were produced from JPEG compression, even at low compression ratios. However, even after the blocking artifacts were omitted in JPEG compressed images, JPEG2000 outperformed JPEG at low compression levels. We found in this study that the image contrast and the average gray level play important roles in image compression and quality evaluation. There were drawbacks in all metrics that we used. In the future, the image gray level and contrast effect should be considered in developing new objective metrics.

Quality Degradation in Lossy Wavelet Image Compression

The objective of this study was to develop a method for measuring quality degradation in lossy wavelet image compression. Quality degradation is due to denoising and edge blurring effects that cause smoothness in the compressed image. The peak Moran z histogram ratio between the reconstructed and original images is used as an index for degradation after image compression. The Moran test is applied to images randomly selected from each medical modality, computerized tomography, magnetic resonance imaging, and computed radiography and compressed using the wavelet compression at various levels. The relationship between the quality degradation and compression ratio for each image modality agrees with previous reports that showed a preference for mildly compressed images. Preliminary results show that the peak Moran z histogram ratio can be used to quantify the quality degradation in lossy image compression. The potential for this method is applications for determining the optimal compression ratio (the maximized compression without seriously degrading image quality) of an image for teleradiology.

A Blurring Index for Medical Images

This study was undertaken to investigate a useful image blurring index. This work is based on our previously developed method, the Moran peak ratio. Medical images are often deteriorated by noise or blurring. Image processing techniques are used to eliminate these two factors. The denoising process may improve image visibility with a trade-off of edge blurring and may introduce undesirable effects in an image. These effects also exist in images reconstructed using the lossy image compression technique. Blurring and degradation in image quality increases with an increase in the lossy image compression ratio. Objective image quality metrics [e.g., normalized mean square error (NMSE)] currently do not provide spatial information about image blurring. In this article, the Moran peak ratio is proposed for quantitative measurement of blurring in medical images. We show that the quantity of image blurring is dependent upon the ratio between the processed peak of Morans Z histogram and the original image. The peak ratio of Morans Z histogram can be used to quantify the degree of image blurring. This method produces better results than the standard gray level distribution deviation. The proposed method can also be used to discern blurriness in an image using different image compression algorithms.

Scatter correction for 3D PET using beam stoppers combined with dual-energy window acquisition: a feasibility study.

Fully three-dimensional (3D) positron emission tomography (PET) can achieve high sensitivity of coincidence events, but the absence of inter-slice septa inevitably leads to increased scattered events. The scattered events can represent as much as 50% of the total detected events. In this research, we proposed a scatter correction method for 3D PET based on beam stoppers and dual-energy window acquisition. The beam stoppers were placed surrounding the object to attenuate primary beams. The scatter fractions were directly estimated at those blocked lines of response and then the entire scatter fraction distribution was recovered using the dual-energy window ratio as reference. The performance was evaluated by using Monte Carlo simulations of various digital phantoms. For the Utah phantom study, the proposed method accurately estimated the scatter fraction distribution, and improved image contrast and quantification based on four different quality indices as performance measures. For the non-homogeneous Zubal phantom, the simulated results also demonstrated that the proposed method achieved a better restoration of image contrast than the dual-energy window method. We conclude that the proposed scatter correction method could effectively suppress various kinds of scattered events, including multiple scatter and scatter from outside the field of view.

A Novel Image Smoothing Filter Using Membership Function

This paper presents a new class of image noise smoothing algorithms utilizing the membership information of the neighboring pixels. The basic idea of this method is to compute the smoothed output using neighboring pixels from the same cluster to avoid image blurring. A fuzzy c-means algorithm is first applied to the image to separate the image pixels into a certain number of clusters. A membership function is defined as the probability that a pixel belongs to a cluster. The proposed method uses this membership function as a weight to calculate the weighted sum of the pixel values from its neighboring pixels. The results of the application of this algorithm to various images show that it can smooth images with edge enhancement. The smoothness of the resultant images can be controlled by the cluster number and window size.

Determination of beam intensity in a single step for IMRT inverse planning.

In intensity modulated radiotherapy (IMRT), targets are treated by multiple beams at different orientations each with spatially-modulated beam intensities. This approach spreads the normal tissue dose to a greater volume and produces a higher dose conformation to the target. In general, inverse planning is used for IMRT treatment planning. The inverse planning requires iterative calculation of dose distribution in order to optimize the intensity profile for each beam and is very computation intensive. In this paper, we propose a single-step method utilizing a figure of merit (FoM) to estimate the beam intensities for IMRT treatment planning. The FoM of a ray is defined as the ratio between the delivered tumour dose and normal tissue dose and is a good index for the dose efficacy of the ray. To maximize the beam utility, it is natural to irradiate the tumour with intensity of each ray proportional to the value of the FoM. The nonuniform beam intensity profiles are then fixed and the weights of the beam are determined iteratively in order to yield a uniform tumour dose. In this study, beams are employed at equispaced angles around the patient. Each beam with its field size that just covers the tumour is divided into a fixed number of beamlets. The FoM is calculated for each beamlet and this value is assigned to be the beam intensity. Various weighting factors are incorporated in the FoM computation to accommodate different clinical considerations. Two clinical datasets are used to test the feasibility of the algorithm. The resultant dose-volume histograms of this method are presented and compared to that of conformal therapy. Preliminary results indicate that this method reduces the critical organ doses at a small expense of uniformity in tumour dose distribution. This method estimates the beam intensity in one single step and the computation time is extremely fast and can be finished in less than one minute using a regular PC.