Biomedical subjects

Chaozhe Zhu

Publications and source records attributed to Chaozhe Zhu.

5 recordsLinked to original sources

PubMed2006

Discriminative analysis of relapsing neuromyelitis optica and relapsing-remitting multiple sclerosis based on two-dimensional histogram from diffusion tensor imaging.

It is difficult to completely differentiate patients with relapsing neuromyelitis optica (RNMO) from relapsing-remitting multiple sclerosis (RRMS) for their similarities in clinical manifestation. In this study, we proposed a novel approach, using two-dimensional histogram of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the brain derived from diffusion tensor imaging (DTI) as classification feature, to discriminate patients with RNMO from RRMS. In this approach, two-dimensional principal component analysis (2D-PCA) was used to extract feature and reduce dimensionality of matrix-formed data efficiently. Then linear discriminant analysis (LDA) was performed on these extracted features to find the best projection direction to separate patients with RNMO from RRMS. Finally, a minimum distance classifier was generated on the basis of projection scores. The correct recognition rate of our method reached 85.7%, validated by the leave-one-out method. This result was much higher than that using feature of ADC or FA separately (59.5% for ADC, 76.2% for FA). In conclusion, the proposed method on the basis of combined features is more effective for classification than those merely using the features separately, and it may be helpful in differentiating RNMO from RRMS patients.

Diffusion Magnetic Resonance Imaging↗

PubMed2006

Fingerprint registration by maximization of mutual information.

Fingerprint registration is a critical step in fingerprint matching. Although a variety of registration alignment algorithms have been proposed, accurate fingerprint registration remains an unresolved problem. We propose a new algorithm for fingerprint registration using orientation field. This algorithm finds the correct alignment by maximization of mutual information between features extracted from orientation fields of template and input fingerprint images. Orientation field, representing the flow of ridges, is a relatively stable global feature of fingerprint images. This method uses the statistics and distribution of global feature of fingerprint images so that it is robust to image quality and local changes in images. The primary characteristic of this method is that it uses this stable global feature to align fingerprints, and that its behavior may resemble the way humans compare fingerprints. Experimental results show that the occurrence of misalignment is dramatically reduced and that registration accuracy is greatly improved at the same time, leading to enhanced matching performance.

Algorithms↗

PubMed2005

Side and handedness effects on the cingulum from diffusion tensor imaging.

In order to explore the microstructure of bilateral cingulum bundles associated with side and handedness, fractional anisotropy was extracted from diffusion tensor imaging. Distinguished from the conventional region of interest-based method, a fiber-based analysis method called scale-invariant parameterization method was employed to ascertain the anisotropy along the cingulum bundle in 31 normal right-handers and 14 normal left-handers. The statistical results showed a remarkable left-greater-than-right asymmetry pattern of anisotropy in most segments of cingulum bundles except the most posterior segment, for both right-handers and left-handers. Interestingly, higher anisotropy of the right-hander than the left-hander was found in the bilateral cingulum bundles. No significant handedness-by-side interaction was obtained in the present study, however.

Adult↗

PubMed2005

Asymmetry analysis of cingulum based on scale-invariant parameterization by diffusion tensor imaging.

Current analysis of diffusion tensor imaging (DTI) is based mostly on a region of interest (ROI) in an image dataset, which is specified by users. This method is not always reliable, however, because of the uncertainty of manual specification. We introduce an improved fiber-based scheme rather than an ROI-based analysis to study in DTI datasets of 31 normal subjects the asymmetry of the cingulum, which is one of the most prominent white matter fiber tracts of the limbic system. The present method can automatically extract the quantitative anisotropy properties along the cingulum bundles from tractography. Moreover, statistical analysis was carried out after anatomic correspondence specific to the cingulum across subjects was established, rather than the traditional whole-brain registration. The main merit of our method compared to existing counterparts is that to find such anatomic correspondence in cingulum, a scale-invariant parameterization method by arc-angle was proposed. It can give a continuous and exact description on any segment of cingulum. More interestingly, a significant left-greater-than-right asymmetry pattern was obtained in most segments of cingulum bundle (-50-25 degrees), except in the most posterior portion of cingulum (25-50 degrees).

Adult↗

PubMed2003

Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images.

A local image model is proposed to eliminate the adverse impact of both artificial and inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based image segmentation methods. The estimation and correction procedures for intensity inhomogeneities are no longer indispensable because the highly convoluted spatial distribution of different tissues in the brain is taken into consideration. On the basis of the local image model, multicontext fuzzy clustering (MCFC) is proposed for classifying 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid automatically. In MCFC, multiple clustering contexts are generated for each pixel, and fuzzy clustering is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. To maintain the statistical reliability and spatial continuity of membership distributions, a fusion strategy is adopted to integrate the clustering outcomes from different contexts. The fusion result is taken as the final membership value of the pixel. Experimental results on both real MR images and simulated volumetric MR data show that MCFC outperforms the classic fuzzy c-means (FCM) as well as other segmentation methods that deal with intensity inhomogeneities.

Artifacts↗