SVM-based texture classification in optical coherence tomography

This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1

Dual-tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram Method for Palmprint Recognition

In the paper, we improve the Local Binary Pattern Histogram (LBPH) approach and combine it with Dual-Tree Complex Wavelet Transform (DT-CWT) to propose a Dual-Tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram (DT-CWT based LBPWH) method for palmprint representation and recognition. The approximate shift invariant property of the DT-CWT and its good directional selectively in 2D make it a very appealing choice for palmprint representation. LBPH is a powerful texture description method, which considers both shape and texture information to represent an image. To enhance the representation capability of LBPH, a weight set is computed and assigned to the finial feature histogram. Here we needn't construct a palmprint model by a train sample set, which is not like some methods based on subspace discriminant analysis or statistical learning. In the approach, a palmprint image is first decomposed into multiple subbands by using DT-CWT. After that, each subband in complex wavelet domain is divided into non-overlapping sub-regions. Then LBPHs are extracted from each sub-region in each subband, and lastly, all of LBPHs are weighted and concatenated into a single feature histogram to effectively represent the palmprint image. A Chi square distance is used to measure the similarity of different feature histograms and the finial recognition is performed by the nearest neighborhood classifier. A group of optimal parameters is chosen by 20 verification tests on our palmprint database. In addition, the recognition results on our palmprint database and the database from the Hong Kong Polytechnic University show the proposed method outperforms other methods

A Quaternionic Wavelet Transform-based Approach for Object Recognition

Recognizing the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT) is presented. This transform separates the information contained in the image better than a traditional Discrete wavelet transform and provides a multiscale image analysis whose coefficients are 2D analytic, with one near-shift invariant magnitude and three phases. The two phases encode local image shifts and the third one contains texture information. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, patches are extracted over the interest points detected from the original image using Wavelet based interest point detector. Here QWT magnitude and phase features are computed for every patch. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. In order to compare the performance of local feature with global feature, the transform is applied to the entire image and the global features are derived. The performance of QWT is compared with discrete wavelet transform (DWT) and dual tree discrete wavelet transform (DTDWT). Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The experimental evaluation is done using the complex Graz databases.Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 350-357, DOI:http://dx.doi.org/10.14429/dsj.64.450

TRAUMATIC BRAIN INJURY ASSESSMENT USING THE INTEGRATION OF PATTERN RECOGNITION METHODS AND FINITE ELEMENT ANALYSIS

The overall goal of this research is to develop methods and algorithms to investigate the severity of Traumatic brain injury (TBI) and to estimate the intracranial pressure (ICP) level non-invasively. Brain x-ray computed tomography (CT) images and artificial intelligence methods are employed to estimate the level of ICP. Fully anisotropic complex wavelet transform features are proposed to extract directional textural features from brain images. Different feature selection and classification methods are tested to find the optimal feature vector and estimate the ICP using support vector regression. By using systematic feature extraction, selection and classification, promising results on ICP estimation are achieved. The results also indicate the reliability of the proposed algorithm. In the following, case-based finite element (FE) models are extracted from CT images using Matlab, Solidworks, and Ansys software tools. The ICP estimation obtained from image analysis is used as an input to the FE modeling to obtain stress/strain distribution over the tissue. Three in-plane modeling approaches are proposed to investigate the effect of ICP elevation on brain tissue stress/strain distribution. Moreover, the effect of intracranial bleeding on ICP elevation is studied in 2-D modeling. A mathematical relationship between the intracranial pressure and the maximum strain/stress over the brain tissue is obtained using linear regression method. In the following, a 3-D model is constructed using 3 slices of brain CT images. The effect of increased ICP on the tissue deformation is studied. The results show the proposed framework can accurately simulate the injury and provides an accurate ICP estimation non-invasively. The results from this study may be used as a base for developing a non-invasive procedure for evaluating ICP using FE methods

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform(DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images.The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition