Xiaofu He

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Abstract. Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

Association of Cerebral Networks in Resting State with Sexual Preference of Homosexual Men: A Study of Regional Homogeneity and Functional Connectivity

Recent imaging studies have shown that brain morphology and neural activity during sexual arousal differ between homosexual and heterosexual men. However, functional differences in neural networks at the resting state is unknown. The study is to characterize the association of homosexual preference with measures of regional homogeneity and functional connectivity in the resting state. Participants were 26 healthy homosexual men and 26 age-matched healthy heterosexual men in whom we collected echo planar magnetic resonance imaging data in the resting state. The sexual orientation was evaluated using the Kinsey Scale. We first assessed group differences in regional homogeneity and then, taking the identified differences as seed regions, we compared groups in measures of functional connectivity from those seeds. The behavioral significances of the differences in regional homogeneity and functional connectivity were assessed by examining their associations with Kinsey Scores. Homosexual participants showed significantly reduced regional homogeneity in the left inferior occipital gyrus, right middle occipital gyrus, right superior occipital gyrus, left cuneus, right precuneus, and increased regional homogeneity in rectal gyrus, bilateral midbrain, and left temporal lobe. Regional homogeneity correlated positively with Kinsey scores in the left inferior occipital gyrus. The homosexual group also showed reduced functional connectivity between left middle temporal gyrus, left supra-marginal gyrus, right cuneus and the seed region, i.e. left inferior occipital gyrus. Additionly, the connection between the left inferior occipital gyrus and right thalamus correlated positively with Kinsey scores. These differences in regional homogeneity and functional connectivity may contribute to a better understanding of the neural basis of male sexual orientation.

A new segmentation approach for iris recognition based on hand-held capture device

Iris segmentation is an important step for automatic iris recognition. This paper presents a new iris segmentation method for hand-held capture device. We use a geometrical method for pupil detection. The bottom point of pupil is used as the reference point for pupil localization because it is insensitive to pupil dilation and not affected by the top eyelid or eyelashes. To decrease computational cost, the outer (or limbus) boundary of iris is localized based on shrunk image using Hough transform and modified Canny edge detector. The lower part of iris pattern is used for recognition in order to reduce the occlusion by eyelashes and eyelids. Experimental results demonstrate that the proposed method has an encouraging performance.

Statistical texture analysis-based approach for fake iris detection using support vector machines

This paper presents a novel statistical texture analysis based method for detecting fake iris. Four distinctive features based on gray level co-occurrence matrices (GLCM) and properties of statistical intensity values of image pixels are used. A support vector machine (SVM) is selected to characterize the distribution boundary, for it has good classification performance in high dimensional space. The proposed approach is privacy friendly and does not require additional hardware. The experimental results indicate the new approach to be a very promising technique for making iris recognition systems more robust against fake-iris-based spoofing attempts.

A Fake Iris Detection Method Based on FFT and Quality Assessment

In recent years, iris recognition is becoming a very active topic in both research and practical applications. However, fake iris is a potential threat there are potential threats for iris-based systems. This paper presents a novel fake iris detection method based on the analysis of 2-D Fourier spectra together with iris image quality assessment. First, image quality assessment method is used to exclude the defocused, motion blurred fake iris. Then statistical properties of Fourier spectra for fake iris are used for clear fake iris detection. Experimental results show that the proposed method can detect photo iris and printed iris effectively.

A New Fake Iris Detection Method

Recent research works have revealed that it is not difficult to spoof an automated iris recognition system using fake iris such as contact lens and paper print etc. Therefore, it is very important to detect fake iris as much as possible. In this paper, we propose a new fake iris detection method based on wavelet packet transform. First, wavelet packet decomposition is used to extract the feature values which provide unique information for discriminating fake irises from real ones. Second, to enhance the detecting accuracy of fake iris, Support vector machine (SVM) is used to characterize the distribution boundary based on extracted wavelet packet features, for it has good classification performance in high dimensional space and it is originally developed for two-class problems. The experimental results indicate the proposed method is to be a very promising technique for making iris recognition systems more robust against fake iris spoofing attempts.

A novel iris segmentation method for hand-held capture device

In this paper, a new iris segmentation method for Hand-held capture device is proposed. First, the pupil is binarized using the intensity threshold, then use morphologic method to denoise the eyelashes and eyelids noise. The geometrical method is used to calculate the coordinates of the pupil. Second, the outer (or limbus) boundary is localized using the shrunk image with the Hough transform and modified Canny edge detector in order to reduce computational cost. Third, the eyelids which are constrained to be within the outer boundary are estimated using the polynomial fitting method. The segmentation method was implemented and tested on iris database set which is captured by hand-held optical sensor device. Experimental results show that the proposed algorithm can separate the iris from the surrounding noises with good speed and accuracy.

An efficient iris segmentation method for recognition

In this paper, an efficient iris segmentation method for recognition is described. The method is based on crossed chord theorem and zigzag collarette area. We select the zigzag collarette region as personal identification pattern, which can remove unnecessary areas and get good recognition rate. Zigzag collarette area is one of the most important parts of iris complex pattern. It is insensitive to the pupil dilation and not affected by the eyelid or eyelash since it is closed with the pupil. In our algorithm, we could avoid procedure for eyelid detection and searching the radius and the center position of the outer boundary between the iris and the sclera, which is difficult to locate when there is little contrast between iris and sclera regions. The method was implemented and tested using two iris database sets, i.e CASIA and SJTU-IDB, with different contrast quality. The experimental results show that the performance of the proposed method is encouraging and comparable to the traditional method.

AOTF based molecular hyperspectral imaging system and its applications on nerve morphometry

The neuroanatomical morphology of nerve fibers is an important description for understanding the pathological aspects of nerves. Different from the traditional automatic nerve morphometry methods, a molecular hyperspectral imaging system based on an acousto-optic tunable filter (AOTF) was developed and used to identify unstained nerve histological sections. The hardware, software, and system performance of the imaging system are presented and discussed. The gray correction coefficient was used to calibrate the systems spectral response and to remove the effects of noises and artifacts. A spatial-spectral kernel-based approach through the support vector machine formulation was proposed to identify nerve fibers. This algorithm can jointly use both the spatial and spectral information of molecular hyperspectral images for segmentation. Then, the morphological parameters such as fiber diameter, axon diameter, myelin sheath thickness, fiber area, and g-ratio were calculated and evaluated. Experimental results show that the hyperspectral-based method has the potential to recognize and measure the nerve fiber more accurately than traditional methods.

Extraction of complex wavelet features for iris recognition

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transformpsilas properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate.