Fast computation of Zernike moments from normalized geometric moments has been developed in this work. The computation is multiplications free and only additions are needed to generate Zernike moments. Geometric moments have been generated using Hatamian's filter up to high orders by a very simple and straightforward computation scheme. Other kinds of moments (i.e., Legendre, pseudo Zernike. etc.) can be computed using the same algorithm after giving the proper transformations that state their relation to geometric moments. Proper normalizations to geometric moments are necessary so that it can be used in the efficient computation of Zernike moments. To ensure fair comparisons, recursive algorithms are used to generate Zernike polynomials and any other coefficients. Computational complexity model and the test programs show that the speed up factor of the proposed algorithm is superior with respect to other fast and/or direct computations. To our knowledge, it is the first time that Zernike moments can be computed in real time rates which encourages the use of Zernike moments features in different image retrieval systems that supports huge database such as XM experimental model stated for MPEG-7 experimental core. It is concluded that choosing direct computation would be impractical.

The current preoperative way of assessing the degree of malignancy in brain glioma is based on magnetic resonance imaging (MRI) findings and clinical data. We studied 280 cases, of which 111 were high-grade malignancies and 169 low-grade, to acquire regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy. However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Network (FMMNN) was proposed. The performance of Multi-Layer Perceptron network (MLP) trained with error Back-Propagation algorithm (BP), the well-known decision tree algorithm ID3, Nearest Neighbor, and the original Fuzzy Min-Max Neural Network were also evaluated. The results showed that two fuzzy decision rules on only 6 features achieved an accuracy of 84.6% (89.9% for low-grade cases and 76.6% for high-grade ones). Investigations with the proposed algorithm revealed that age, mass effect, edema, post–contrast enhancement, blood supply, calcification, hemorrhage, and signal intensity of the T1-weighted Image were important diagnostic factors.

Automatic identification of landmarks in cephalometry is very important and useful for orthognathic surgery. In this paper, a computerized automatic cephalometric analysis system (CACAS) based on image processing is presented. For original X-ray image, median filtering and histogram equalization is used to improve image quality. The edge of a X-ray image is detected by wavelet transform and Canny filter. Seventeen landmarks in cephalometry are successfully identified by knowledge-based edge tracing and changeable template. Seventy three measurements based on distances, angles, ratios among landmarks are computed automatically. The reliability of landmarks and the validity of measurements are compared between automatic and manual operation. The values of measurements by CACAS are preciser and more reliable, the mean error for linear measurements is less than 0.9mm; the mean error for angular measurements is less than 1.2dg. The rate of validity is over 80% even if the image quality is poor. For an image with high signal to noise ratio, the rate of validity of landmarking and measurements by the CACAS system is over 90%.

Clustering analysis is an important topic in artificial intelligence (AI) and pattern recognition (PR) research. Conventional clustering algorithms, such as the famous Fuzzy C-Means clustering algorithm (FCM) assume that all the attributes are equally relevant to all the clusters. However in most domains, some attributes are irrelevant, and some relevant ones are less important than others for a specific class. In this paper, such imbalance between the attributes is considered and a new weighted fuzzy kernel-clustering algorithm WFKCA is presented. WFKCA performs clustering in high feature space mapped by mercer kernels. Comparing with the conventional hard kernel-clustering algorithm, WFKCA can derive the meaningful prototypes of the clusters. Numerical convergence properties of WFKCA are also discussed. In order to tackle with the incomplete data effectively, we extend WFKCA to WFKCA2, which is demonstrated a useful tool for clustering incomplete data. Finally, we further demonstrate WFKCA is an effective tool for image segmentation with numerical examples.

Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.