Zhishun She

Combination of features from skin pattern and ABCD analysis for lesion classification

Background/Purpose: It is known that the standard features for lesion classification are ABCD features, that is, asymmetry, border irregularity, colour variegation and diameter of lesion. However, the observation that skin patterning tends to be disrupted by malignant but not by benign skin lesions suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using both skin line direction and intensity for lesion classification was encouraging. But these features have not been combined with the ABCD features. This paper explores the possibility of combing features from skin pattern and ABCD analysis to enhance classification performance.

Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing

Three-dimensional (3D) synthetic aperture radar (SAR) imaging via multiple-pass processing is an extension of interferometric SAR imaging. It exploits more than two flight passes to achieve a desired resolution in elevation. In this paper, a novel approach is developed to reconstruct a 3D space-borne SAR image with multiple-pass processing. It involves image registration, phase correction and elevational imaging. An image model matching is developed for multiple image registration, an eigenvector method is proposed for the phase correction and the elevational imaging is conducted using a Fourier transform or a super-resolution method for enhancement of elevational resolution. 3D SAR images are obtained by processing simulated data and real data from the first European Remote Sensing satellite (ERS-1) with the proposed approaches.

Three-dimensional SAR imaging via multiple pass processing

This paper develops a novel approach to reconstruct a three-dimensional (3D) SAR image with multiple pass processing. It involves image registration, phase correction and beamforming in elevation. An eigenvector method is proposed for the phase correction and the beamforming in elevation is carried out by a DFT or a subspace method for superresolution. 3D SAR images are demonstrated by processing ERS-1 real data with the proposed approach.

ISAR motion compensation using modified Doppler centroid tracking method

With regard to the phase compensation in inverse synthetic aperture radar (ISAR), the modified Doppler centroid tracking (MDCT) method is developed which applies the phase gradient autofocus (PGA) algorithm developed by Wahl (1994) to improve the Doppler centroid tracking (DCT) method. When the phase compensation is performed, the proposed approach smartly eliminates the effect of the rotational phase component (RPC) on the estimation of the translational phase component (TPC) by circular shifting, windowing and iteration steps. After several iterations, the maximum likelihood estimation and compensation of the TPC of the target can be realized more effectively. The processing results of live data show that the proposed method can improve the imaging quality of ISAR significantly.

Analysis of skin line pattern for lesion classification

Background/purpose: It has been observed that skin patterning tends to be disrupted by malignant but not by benign skin lesions. This suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using a measurement of line strength by a consistent high‐value profiling technique followed by local variance measurement or a region agglomerative classifier to measure skin line pattern disruption was extremely promising but computationally intensive, suggesting that the idea of measuring skin pattern disruption was useful but a simpler method was required.

Detection of Psychological Stress Using a Hyperspectral Imaging Technique

The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference

Autofocus for ISAR Imaging Using Higher Order Statistics

p\< 0.005

Cross-range scaling of inverse synthetic aperture radar

and a large practical discrimination (d 1/4 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances.

Autofocus for inverse synthetic aperture radar (ISAR) imaging

Autofocus is imperative for inverse synthetic aperture radar (ISAR) imaging. In this letter, a new approach for ISAR autofocus is developed by using fourth-order statistics properties of the radarpsilas return signal. After the ISAR signal model is established, the approach is described. The results of processing real data confirm the effectiveness of the proposed approach and show its capability for suppressing noise. The developed approach has a numerical stability and a smaller computational load compared with the maximum image contrast and the minimum image entropy methods.

Skin pattern analysis for lesion classification using local isotropy.

This paper investigates the problem of cross-range scaling of inverse synthetic aperture radar. It is very important to target classification and recognition via ISAR. The cross-range scale of ISAR depends on both radar wavelength and rotating angle of target relative to radar-line-of-sight (RLOS) during the total coherent processing time. The former is known while the latter is difficult to determine especially in the case of ISAR. In this paper, a new approach, which is based on the principle of tomographic imaging and the property of the coherent processing of echo data, is developed to estimate the rotating angle of target relative to RLOS during the total coherent processing time. As such,it is able to carry out the cross-range scaling of ISAR. The proposed approach is used to process the computer simulated data and the real data of model B-52 collected in a microwave anechoic chamber. The processing results show that the proposed approach is correct and effective.<<ETX>>