Qingtang Su

A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel

Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimers disease (AD) and mild cognitive impairment (MCI) on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.

Robust color image watermarking technique in the spatial domain

This paper proposes a new blind watermarking algorithm, which embedding the binary watermark into the blue component of a RGB image in the spatial domain, to resolve the problem of protecting copyright. For embedding watermark, the generation principle and distribution features of direct current (DC) coefficient are used to directly modify the pixel values in the spatial domain, and then four different sub-watermarks are embedded into the different areas of the host image for four times, respectively. When watermark extraction, the sub-watermark is extracted with blind manner according to DC coefficients of watermarked image and the key-based quantization step, and then the statistical rule and the method of “first to select, second to combine” are proposed to form the final watermark. Hence, the proposed algorithm is executed in the spatial domain rather than in discrete cosine transform (DCT) domain, which not only has simple and quick performance of the spatial domain but also has high robustness feature of DCT domain. The experimental results show that the proposed watermarking algorithm can obtain better invisibility of watermark and stronger robustness for common attacks, e.g., JPEG compression, cropping, and adding noise. Comparison results also show the advantages of the proposed method.

An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation

Image segmentation is a crucial step in image processing, especially for medical images. However, the existence of partial volume effect, noise and other artifacts makes this problem much more complex. Fuzzy c-means (FCM), as an effective tool to deal with partial volume effect, cannot deal with noise and other artifacts. In this paper, one modified FCM algorithm is proposed to solve the above problems, which includes three main steps: (1) peak detection is used to initialize cluster centers, which can make the initial centers close to the final ones and in turn decrease the number of iterations; (2) fuzzy clustering incorporating spatial information is implemented, which can make the algorithm robust to image artifacts; (3) the segmentation results are refined further by detecting and reallocating the misclassified pixels. Experiments are performed on both synthetic and medical images, and the results show that our proposed algorithm is more effective and reliable than other FCM-based algorithms.

A Heat Kernel Based Cortical Thickness Estimation Algorithm

Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the grey matter geometry information in the in vivo brain MR images. First, we use the harmonic energy function to establish the tetrahedral mesh matching with the MR images and generate the Laplace-Beltrami operator matrix which includes the inherent geometric characteristics of the tetrahedral mesh. Second, the isothermal surfaces are computed by the finite element method with the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cerebral cortex thickness information between the point on the outer surface and the corresponding point on the inner surface. The method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimers disease (AD) and mild cognitive impairment (MCI) on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results in 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm successfully detects statistically significant difference among patients of AD, MCI and healthy control subjects. The results also indicate that the new method may have better performance than the Freesurfer software.

A novel blind color image watermarking based on Contourlet transform and Hessenberg decomposition

In this paper, a novel blind color image watermarking based on Contourlet transform and Hessenberg decomposition is proposed to protect digital copyright of color image. Firstly, each color channel of the host image is transformed by Contourlet transform and its low frequency sub-band is divided into 4 × 4 non-overlap coefficient block. Secondly, the coefficient block selected by MD5-based Hash pseudo-random algorithm is decomposed by Hessenberg decomposition. Thirdly, the watermark information permuted by Arnold transform is embedded into the biggest energy element of the upper Hessenberg matrix by quantization technique. In extraction process, the quantization strength is used for blindly extracting watermark information from the attacked host image without the help of any original image. The results show that the proposed scheme has higher imperceptibility and robustness against most common image attacks in comparison with other related methods.

Fractional quaternion cosine transform and its application in color image copy-move forgery detection

In this paper, fractional quaternion cosine transforms (FrQCT) is proposed to generalize the conventional fractional cosine transforms (FrCT) to quaternion signal processing in a holistic manner. Firstly, the new transform FrQCT is defined and the proof of its inverse transform is presented. An efficient discrete implementation method of FrQCT is then proposed, in which the relationship between FrQCT and FrCT of four components is used for a quaternion signal. Finally, a new color image copy-move forgery detection algorithm based on FrQCT and modified PatchMatch matching algorithm is proposed to evaluate the performance of the proposed FrQCT. Experimental results on two public datasets (FAU dataset and GRIP dataset) demonstrate that: (a) the proposed efficient implementation method takes only half the computational time of the direct method; (b) the proposed FrQCT-based forgery detection algorithm can achieve a better performance than some state-of-the-art algorithms, especially in the additional operation case.

A new watermarking scheme for colour image using QR decomposition and ternary coding

At present, the binary images are often used as the original watermark images of many watermarking methods, but partial methods cannot be easily extended to colour image watermarking methods. For resolving this problem, we propose a new watermarking method using ternary coding and QR decomposition for colour image. In the procedure of embedding watermark, the colour image watermark is coded to ternary information; the colour host image is also separated into image blocks of sized 3 × 3, and these image blocks are further decomposed via QR decomposition; then, one ternary watermark is embedded into one orthogonal matrix Q of QR decomposition by the proposed rules. In the procedure of extracting watermark, the proposed method uses the blind-manner to extract the embedded ternary information. The novelty of this scheme lies in the proposed ternary coding for watermark image, which can improve the imperceptibility, embedded watermark capacity and real-time feature of the watermarking scheme. The results of simulation show the presented technique is better than other compared schemes with respect to imperceptibility, embedded watermark capacity and real-time feature under the similar robustness.