Chien-Shun Lo

Real-Time Simplex Growing Algorithms for Hyperspectral Endmember Extraction

The simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) and shown to be a promising endmember extraction technique. This paper further extends the SGA to a versatile real-time (RT) processing algorithm, referred to as RT SGA, which can effectively address the following four major issues arising in the practical implementation for N-FINDR: (1) use of random initial endmembers which causes inconsistent final results; (2) high computational complexity which results from an exhaustive search for finding all endmembers simultaneously; (3) requirement of dimensionality reduction because of large data volumes; and (4) lack of RT capability. In addition to the aforementioned advantages, the proposed RT SGA can also be implemented by various criteria in endmember extraction other than the maximum simplex volume.

Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery

Subpixel detection in multispectral imagery presents a chal- lenging problem due to relatively low spatial and spectral resolution. We present a generalized constrained energy minimization (GCEM) ap- proach to detecting targets in multispectral imagery at subpixel level. GCEM is a hybrid technique that combines a constrained energy mini- mization (CEM) method developed for hyperspectral image classification with a dimensionality expansion (DE) approach resulting from a gener- alized orthogonal subspace projection (GOSP) developed for multispec- tral image classification. DE enables us to generate additional bands from original multispectral images nonlinearly so that CEM can be used for subpixel detection to extract targets embedded in multispectral im- ages. CEM has been successfully applied to hyperspectral target detec- tion and image classification. Its applicability to multispectral imagery is yet to be investigated. A potential limitation of CEM on multispectral imagery is the effectiveness of interference elimination due to the lack of sufficient dimensionality. DE is introduced to mitigate this problem by expanding the original data dimensionality. Experiments show that the proposed GCEM detects targets more effectively than GOSP and CEM without dimensionality expansion.

Classification of clustered microcalcifications using a Shape Cognitron neural network

A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications. The architecture of S-Cognitron consists of two modules and an extra layer, called 3D figure layer lies in between. The first module contains a shape orientation layer, built with 20 cell planes of low level universal shape features to convert first-order shape orientations into numeric values, and a complex layer, to extract second-order shape features. The 3D figure layer is a feature extract-display layer that extracts the shape curvatures of an input pattern and displays them as a 3D figure. It is then followed by a second module made up of a feature formation layer and a probabilistic neural network-based classification layer. The system is evaluated by using Nijmegen mammogram database and experimental results show that sensitivity and specificity can reach 86.1 and 74.1%, respectively.

Growth-Competition-Based Stem Diameter and Volume Modeling for Tree-Level Forest Inventory Using Airborne LiDAR Data

An individual tree within a forest stand will have its height and diameter growth restricted by the influence of neighboring trees. This is because trees in close proximity compete for resources and space to enable growth. In this paper, the position of trees, tree height (LH), tree crown radius (LCR), and growth competition index (LCI) were extracted from a light-detection-and-ranging (LiDAR)-based rasterized canopy height model using the multilevel morphological active-contour algorithm. The diameter and volume of individual trees are tested and validated to be an exponential function of those LiDAR-derived tree parameters. The best LiDAR-based diameter estimation model and volume estimation model were tested as significant with an R2 value of 0.84 and 0.9 and evaluated with an estimation bias of 8.7 cm and 0.91 m3, respectively. Results also showed that LH and LCR are positively related to the LiDAR-derived diameter at breast height (DBH) and the LiDAR-derived volume of individual trees in a forest stand, whereas LCI is negatively related. The proposed algorithm of individual tree volume estimation was further applied to predict the volume of three sample plots in mountainous forest stands. It was found that the LVM could be used to predict an acceptable volume estimate of old-aged forest stands. The estimation bias, i.e., percentage RMSE (RMSE%), is averaged at around 4% using the LiDAR metrics lnLH, LCI, and LCR, whereas the RMSE% increases to 50% if only lnLH is applied. Results suggest that LCI is an important regulation factor in the estimation of forest volume stocks using LiDAR remote sensing.

3-D localization of clustered microcalcifications using cranio-caudal and medio-lateral oblique view

This paper presents a 3D localization method to register clustered microcalcifications on mammograms from cranio-caudal (CC) and medio-lateral oblique (MLO) views. The method consists of three major components: registration of clustered microcalcifications in CC and MLO views, 3D localization of clustered microcalcifications and 3D visualization of clustered microcalcifications. The registration is performed based on three features, gradient, energy and local entropy codes that are independent of spatial locations of microcalcifications in two different views and are prioritized by discriminability in a binary decision tree. The 3D localization is determined by a sequence of coordinate corrections of calcified pixels using the breast nipple as a controlling point. Finally, the 3D visualization implements a virtual reality modeling language viewer (VRMLV) to view the exact location of the lesion as a guide for needle biopsy. In order to validate our proposed 3D localization system, a set of breast lesions, which appear both in mammograms and in MR Images is used for experiments where the depth of clustered microcalcifications can be verified by the MR images.

Computer-aided diagnostic detection system of venous beading in retinal images

The detection of venous beading in retinal images provides an early sign of diabetic retinopathy and plays an important role as a preprocessing step in diagnosing ocular diseases. We present a computer-aided diagnostic system to automatically detect venous bead- ing of blood vessels. It comprises of two modules, referred to as the blood vessel extraction module (BVEM) and the venus beading detection module (VBDM). The former uses a bell-shaped Gaussian kernel with 12 azimuths to extract blood vessels while the latter applies a neural network-based shape cognitron to detect venous beading among the extracted blood vessels for diagnosis. Both modules are fully computer- automated. To evaluate the proposed system, 61 retinal images (32 beaded and 29 normal images) are used for performance evaluation.

Spectral Feature Probabilistic Coding for Hyperspectral Signatures

Spectral signature coding (SSC) is generally performed by encoding spectral values of a signature across its spectral coverage followed by the Hamming distance to measure signature similarity. The effectiveness of such an SSC largely relies on how well the Hamming distance can capture spectral variations that characterize a signature. Unfortunately, in most cases, this Hamming-distance-based SSC does not provide sufficient discriminatory information for signature analysis because the Hamming distance does not take into account the band-to-band variation, in which case the Hamming distance can be considered as a memoryless distance. This paper extends the Hamming-distance-based SSC to an approach, referred to as spectral feature probabilistic coding (SFPC), which introduces a new concept into SSC that uses a criterion with memory to measure spectral similarity. It implements the well-known arithmetic coding (AC) in two ways to encode a signature in a probabilistic manner, called circular SFPC and split SFPC. The values resulting from the AC is then used to measure the distance between two spectral signatures. In order to demonstrate advantages of using AC-based SSC in signature analysis, a comparative analysis is also conducted against spectral binary coding.

Contrast enhancement and tissues classification of breast MRI using Kalman filter-based linear mixing method

Much attention is currently focused on one of the newest breast examination techniques, breast MRI. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper therefore proposes the use of spectral signature detection technology, the Kalman filter-based linear mixing method (KFLM), which can successfully present the results as high-contrast images and classify breast MRIs into major tissues from four bands of breast MRIs. A series of experiments using phantom and real MRIs was conducted and the results compared with those of the commonly used c-means (CM) method and dynamic contrast-enhanced (DCE) breast MRIs for performance evaluation. After comparison with the CM algorithm and DCE breast MRIs, the experimental results showed that the high-contrast images generated by the spectral signature detection technology, the KFLM, were of superior quality.

Automatic detection of microcalcifications in digital mammograms by entropy thresholding

A system for automatically detecting microcalcifications in digital mammograms is presented. The proposed system, based on a sequence of preprocessing steps (gradient enhancement, mean contrast enhancement and Gaussian blurring-deblurring process) followed by segmentation using entropy thresholding, can effectively and efficiently detect the suspicious microcalcifications. The performance of the designed system is evaluated by skilled radiologists and shows encouraging results.

Fractal Based JPEG2000 ROI Coding

In this paper, we propose a new region-of-interest (ROI) coding method called fractal based JPEG2000 ROI coding for the further improvement of JPEG2000 used in mammograms. This method provides the advantage that it is not necessary for user to provide any ROI information before coding.