In this paper, we propose a new fusion scheme for multi-focus images based on some special edge information called discriminative edge points. A new focus measure is also proposed in this paper to identify which pixels in the source images will be included in the resultant image by evaluating the sum of neighborhood energy of the discriminative edge points within an adaptive focus-measuring window whose center is the location of the pixel under current consideration. Experimental results show that our fusion scheme outperforms other methods in the literature.

This paper presents an automated system for grading pathological images of prostatic carcinoma based on a set of texture features extracted by multi-categories of methods including multi-wavelets, Gabor-filters, GLCM, and fractal dimensions. We apply 5-fold cross-validation procedure to a set of 205 pathological prostate images for training and testing. Experimental results show that the fractal dimension (FD) feature set can achieve 92.7% of CCR without feature selection and 94.1% of CCR with feature selection by using support vector machine classifier. If features of multi-categories are considered and optimized, the CCR can be promoted to 95.6%. The CCR drops to 92.7% if FD-based features are removed from the combined feature set. Such a result suggests that features of FD category have significant contributions and should be included for consideration if features are selected from multi-categories.

Accurate grading for prostatic carcinoma in pathological images is important to prognosis and treatment planning. Since human grading is always time-consuming and subjective, this paper presents a computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues. We proposed two feature extraction methods based on fractal dimension to analyze variations of intensity and texture complexity in regions of interest. Each image can be classified into an appropriate grade by using Bayesian, k-NN, and support vector machine (SVM) classifiers, respectively. Leave-one-out and k-fold cross-validation procedures were used to estimate the correct classification rates (CCR). Experimental results show that 91.2%, 93.7%, and 93.7% CCR can be achieved by Bayesian, k-NN, and SVM classifiers, respectively, for a set of 205 pathological prostate images. If our fractal-based feature set is optimized by the sequential floating forward selection method, the CCR can be promoted up to 94.6%, 94.2%, and 94.6%, respectively, using each of the above three classifiers. Experimental results also show that our feature set is better than the feature sets extracted from multiwavelets, Gabor filters, and gray-level co-occurrence matrix methods because it has a much smaller size and still keeps the most powerful discriminating capability in grading prostate images.

Recently, many applications increasingly demand content-based image retrieval. To speed up the search process, a signature file containing the signatures associated with database images is frequently used as a filter to prune off non-promising images at the early stage of query processing. We generate the signature for each picture with respect to "key" objects and propose a novel structure for organizing signatures. By using this new indexing structure, the number of signatures to be examined per query is reduced significantly. In particular, the number of bits to be examined per query is fewer as compared to the signature tree method proposed by Y. Chen (see Processing Letters, vol.82, p.213-21, 2002)

We present an efficient and effective digital watermarking method for image tamper protection and recovery. Our method is efficient as it only uses simple operations such as parity check and the comparison between the average intensities. It is effective because the detection is hierarchically structured such that the accuracy of tamper localization can be ensured. That is, if a tampered block, as small as 2/spl times/2 pixels, is not detected in level-1 inspection, it will be detected in level-2 or level-3 inspection with probability nearly 1. Our method is also very storage effective, as it only requires a secret key K and a public chaotic mixing algorithm to recover a tampered image. Compared with the method in Celik et al (IEEE Trans. Image Proc., vol. 11, no. 4, pp. 585-594, 2002), our method not only is as simple and as effective in tamper detection and localization, it also provides the capability of tamper recovery.

Spatial relationships between objects are important features for designing a content-based image retrieval system. We propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on minimum bounding rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent /spl sim/ 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.