Wavelet-based Texture Analysis Research Articles

Development of Dermatological Skin Disease Detection and Classification based on Wavelet and ANN

One of the most common and augmenting health problems in the world are related to skin. The most unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection and classification phase.

Review: Dermatological Skin Disease Detection and Classification Based on Wavelet Transform and Artificial Neural Network

One of the most common and augmenting health problems in the world are related to skin. The most unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection and classification phase.

Validation of Macular Choroidal Thickness Measurements from Automated SD-OCT Image Segmentation.

Spectral domain optical coherence tomography (SD-OCT) imaging permits in vivo visualization of the choroid with micron-level resolution over wide areas and is of interest for studies of ocular growth and myopia control. We evaluated the speed, repeatability, and accuracy of a new image segmentation method to quantify choroid thickness compared to manual segmentation. Two macular volumetric scans (25 × 30°) were taken from 30 eyes of 30 young adult subjects in two sessions, 1 hour apart. A single rater manually delineated choroid thickness as the distance between Bruch's membrane and sclera across three B-scans (foveal, inferior, and superior-most scan locations). Manual segmentation was compared to an automated method based on graph theory, dynamic programming, and wavelet-based texture analysis. Segmentation performance comparisons included processing speed, choroid thickness measurements across the foveal horizontal midline, and measurement repeatability (95% limits of agreement (LoA)). Subjects were healthy young adults (n = 30; 24 ± 2 years; mean ± SD; 63% female) with spherical equivalent refractive error of -3.46 ± 2.69D (range: +2.62 to -8.50D). Manual segmentation took 200 times longer than automated segmentation (780 vs. 4 seconds). Mean choroid thickness at the foveal center was 263 ± 24 μm (manual) and 259 ± 23 μm (automated), and this difference was not significant (p = 0.10). Regional segmentation errors across the foveal horizontal midline (±15°) were ≤9 μm (median) except for nasal-most regions closest to the nasal peripapillary margin-15 degrees (19 μm) and 12 degrees (16 μm) from the foveal center. Repeatability of choroidal thickness measurements had similar repeatability between segmentation methods (manual LoA: ±15 μm; automated LoA: ±14 μm). Automated segmentation of SD-OCT data by graph theory and dynamic programming is a fast, accurate, and reliable method to delineate the choroid. This approach will facilitate longitudinal studies evaluating changes in choroid thickness in response to novel optical corrections and in ocular disease.

Using unmanned aerial vehicles for high-resolution remote sensing to map invasive Phragmites australis in coastal wetlands

ABSTRACTThe wetland plant species, Phragmites australis, is present on every continent except Antarctica. Both native and non-native subspecies thrive in the USA with the non-natives quickly displacing native wetland plants. Along the Gulf Coast, Phragmites grows in very dense stands, and at heights of greater than 4.6 m, is usually the tallest grass species in a wetland, estuary, and marsh ecosystems. Phragmites is known to alter the ecology of these wetland systems making them less suitable as habitat for many species of flora and fauna. Furthermore, Phragmites presents a navigation hazard to smaller boats by impairing visibility along shorelines and around bends of canals and rivers. Management efforts targeting non-native Phragmites rely heavily on accurately mapping invaded areas. Historically, mapping has been done through walking the perimeter of a stand with a Global Positioning System (GPS) unit, using satellite imagery, or through the use of aerial photography from manned aircraft. These methods are time consuming, are expensive, can have an inadequate resolution, and in some cases are prone to human error. In this work, an Unmanned Aerial System (UAS) was used to capture visible imagery to create a basin-wide distribution map of a large wetland along the US Pearl River delta in southeastern Louisiana. The imagery was collected in the summer and individual images were mosaicked to create a larger map. We then evaluated the use of texture analysis on the mosaics to automatically map the invasive. Specifically, Gabor filters, grey level co-occurrence matrices, segmentation-based fractal texture analysis, and wavelet-based texture analysis were compared for mapping the Phragmites. Our experimental results, conducted using the imagery we collected over four study areas (approximately 2250 ha) along the US Pearl River delta, indicate the proposed texture-based approach yields an average accuracy of 85%, an average kappa accuracy of 70%. These maps have shown to be very useful for resource managers to hasten the eradication efforts of Phragmites.

English

Texture analysis is quite important operation in medical image processing. Wavelet based texture analysis is developed which gives better way of extracting inherent object and properties in medical image. It gives a robust technique in disease diagnosis and future of biophysical disorder ness in a body. Image processing has an important influence on the medical decision making process and even on surgical actions. In this paper the importance of texture analysis of medical images with Wavelets are discussed and its application.

FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING SDOST

Finger knuckle-print biometric system has widely us ed in modern e-world. The region of interest is needed as the key for the feature extraction in a g ood biometric system. The symmetric discrete orthonormal stockwell transform provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture feature s in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods. Thi s motivates us to propose a new local and global feature extractor. For the finger knuckle-print, th e local and global features are critical for an ima ge observation and recognition. For the finger knuckle -print, the local and global information are critic al for an image observation and recognition. The local features of an enhanced finger knuckle-print image are extracted using symmetric discrete orthon ormal stockwell transform. The Fourier transform of an image is obtained by increasing the scale of symmetric discrete orthonormal stockwell transform to infinity. The Fourier transform coefficients ext racted from the finger knuckle-print image is considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The gl obal feature is exploited to refine the arrangement of finger knuckle-print images in matching. The pro posed scheme makes use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance o f two finger knuckle-print images. The investigational results indicate that the proposed work outperforms an existing works with an equal error rate of 0.0045 and 100% correct recognition r ate on the finger knuckle-print database.

Parametric Performance Evaluation of Wavelet-Based Corrosion Detection Algorithms for Condition Assessment of Civil Infrastructure Systems

Corrosion is a crucial defect in structural systems that can lead to catastrophic effects if neglected. Current structure inspection standards require an inspector to visually assess the conditions of a target structure. A less time-consuming and inexpensive alternative to current monitoring methods is to use a robotic system, which can inspect structures more frequently and perform autonomous damage detection. The feasibility of using image processing techniques to detect corrosion in structures has been acknowledged by leading experts in the field; however, there has not been a systematic study to evaluate the effects of different parameters on the performance of vision-based corrosion detection systems. This study evaluates several parameters that can affect the performance of color wavelet-based texture analysis algorithms for detecting corrosion. Furthermore, an approach is proposed to utilize the depth perception for corrosion detection. The proposed approach improves the reliability of the corrosion detection algorithm. The integration of depth perception with pattern classification algorithms, which has never been reported in published studies, is part of the contribution of the current study. Several quantitative evaluations are presented to scrutinize the performance of the investigated approaches.

Wavelet Variance Analysis for Random Fields on a Regular Lattice

There has been considerable recent interest in using wavelets to analyze time series and images that can be regarded as realizations of certain 1-D and 2-D stochastic processes on a regular lattice. Wavelets give rise to the concept of the wavelet variance (or wavelet power spectrum), which decomposes the variance of a stochastic process on a scale-by-scale basis. The wavelet variance has been applied to a variety of time series, and a statistical theory for estimators of this variance has been developed. While there have been applications of the wavelet variance in the 2-D context (in particular, in works by Unser in 1995 on wavelet-based texture analysis for images and by Lark and Webster in 2004 on analysis of soil properties), a formal statistical theory for such analysis has been lacking. In this paper, we develop the statistical theory by generalizing and extending some of the approaches developed for time series, thus leading to a large-sample theory for estimators of 2-D wavelet variances. We apply our theory to simulated data from Gaussian random fields with exponential covariances and from fractional Brownian surfaces. We demonstrate that the wavelet variance is potentially useful for texture discrimination. We also use our methodology to analyze images of four types of clouds observed over the southeast Pacific Ocean.

Wavelet based texture analysis and classification of bone lesions from dental CT

The research presented in this article is aimed at developing a computerised automatic imaging system for classification of bone samples in dental CT. The article focuses on using texture analysis for the classification of bone samples from CT scans. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest in the CT medical images and creation of a classifier. A comparative study of wavelets-based texture descriptors from three families of wavelets (Daubechies', coiflets, symlets) coupled with the implementation of a decision tree classifier based on back propagation is carried on. Principal component analysis is compared against the classical sequential methods in terms of the best recognition rate achieved and the optimal number of features. The classification performance of 100% is achieved in PCA with optimal features compared to sequential methods.

Image Texture Characterization Using the Discrete Orthonormal S-Transform

We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods.

Wavelet-based texture analysis and synthesis using hidden Markov models

Wavelet-domain hidden Markov models (HMMs), in particular, hidden Markov tree (HMT), were recently proposed and applied to image processing, where it was usually assumed that three subbands of the two-dimensional discrete wavelet transform (DWT), i.e., HL, LH, and HH, are independent. In this paper, we study wavelet-based texture analysis and synthesis using HMMs. Particularly, we develop a new HMM, called HMT-3S, for statistical texture characterization in the wavelet domain. In addition to the joint statistics captured by HMT, the new HMT-3S can also exploit the cross correlation across DWT subbands. Meanwhile, HMT-3S can be characterized by using the graphical grouping technique, and has the same tree structure as HMT. The proposed HMT-3S is applied to texture analysis, including classification and segmentation, and texture synthesis with improved performance over HMT. Specifically, for texture classification, we study four wavelet-based methods, and experimental results show that HMT-3S provides the highest percentage of correct classification of over 95% upon a set of 55 Brodatz textures. For texture segmentation, we demonstrate that more accurate texture characterization from HMT-3S allows the significant improvements in terms of both classification accuracy and boundary localization. For texture synthesis, we develop an iterative maximum likelihood-based texture synthesis algorithm which adopts HMT or HMT-3S to impose the joint statistics of the texture DWT, and it is shown that the new RMT-3S enables more visually similar results than HMT does.