Breast cancer detection from thermal images using bispectral invariant features

Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

Spectral approximations to the fractional integral and derivative

In this paper, the spectral approximations are used to compute the fractional integral and the Caputo derivative. The effective recursive formulae based on the Legendre, Chebyshev and Jacobi polynomials are developed to approximate the fractional integral. And the succinct scheme for approximating the Caputo derivative is also derived. The collocation method is proposed to solve the fractional initial value problems and boundary value problems. Numerical examples are also provided to illustrate the effectiveness of the derived methods.

Feature-domain super-resolution for iris recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the acquired iris images, which significantly degrades iris recognition performance. Super-resolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, most existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values, rather than the actual features used for recognition. This paper thoroughly investigates transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. A framework for applying super-resolution to nonlinear features in the feature-domain is proposed. Based on this framework, a novel feature-domain super-resolution approach for the iris biometric employing 2D Gabor phase-quadrant features is proposed. The approach is shown to outperform its pixel domain counterpart, as well as other feature domain super-resolution approaches and fusion techniques.

Spectroscopic studies of the adsorption and reactions of chlorofluorocarbons (CFC-11 and CFC-12) and hydrochlorofluorocarbon (HCFC-22) on oxide surfaces

Raman and Fourier transform infrared (FT-IR) spectroscopy have been applied to a systematic investigation of the adsorption and decomposition of dichlorodifluoromethane (CCl2F2, CFC-12), fluorotrichloromethane (CCl3F, CFC-11), chlorodifluoromethane (CHClF2, HCFC-22) and molecular chlorine on oxide surfaces. Additionally, the effects of heating and ultraviolet photolysis of the CFC and HCFCs adsorbed on the oxide surfaces have been investigated. Spectral features for these species indicated a small wavenumber shift (1-6 cm-1) associated with the adsorbed phase. Some evidence, specifically the appearance of the Raman band at 507 cm-1, is presented to show that chlorine decomposition species are associated with these oxide surfaces. It was concluded that the new spectral feature (at ca. 507 cm-1) related with the decomposition of the CFC and HCFC molecules was an important indicator of the extent to which the reaction between the adsorbed CFC and HCFC and oxide surface has taken place. The extent of CFC-surface interaction has been quantified in terms of a maximum (Raman) frequency shift parameter (AM). Wavenumber shifts suggest both cation-adsorbate and non-specific adsorption interactions are occurring in the internal channels of the zeolites. Slow decomposition of the adsorbed CFCs under ultraviolet-visible photolysis (at ? > 300 nm) and/or thermal treatment was observed spectroscopically. Using FT-IR spectroscopy, the formation of gas-phase products (CO, CO2, HCl) both onyn photolysis and heating was evident. Results of these measurements are compared with the observed atmospheric reactivity of these compounds.

Visual guidance for fixed-wing unmanned aerial vehicles using feature tracking : application to power line inspection

This thesis presents novel vision based control solutions that enable fixed-wing Unmanned Aerial Vehicles to perform tasks of inspection over infrastructure including power lines, pipe lines and roads. This is achieved through the development of techniques that combine visual servoing with alternate manoeuvres that assist the UAV in both following and observing the feature from a downward facing camera. Control designs are developed through techniques of Image Based Visual Servoing to utilise sideslip through Skid-to-Turn and Forward-Slip manoeuvres. This allows the UAV to simultaneously track and collect data over the length of infrastructure, including straight segments and the transition where these meet.

The spectroscopic characterization of the sulphate mineral ettringite from Kuruman manganese deposits, South Africa

The mineral ettringite has been studied using a number of techniques, including XRD, SEM with EDX, thermogravimetry and vibrational spectroscopy. The mineral proved to be composed of 53% of ettringite and 47% of thaumasite in a solid solution. Thermogravimetry shows a mass loss of 46.2% up to 1000 °C. Raman spectroscopy identifies multiple sulphate symmetric stretching modes in line with the three sulphate crystallographically different sites. Raman spectroscopy also identifies a band at 1072 cm−1 attributed to a carbonate symmetric stretching mode, confirming the presence of thaumasite. The observation of multiple bands in the ν4 spectral region between 700 and 550 cm−1 offers evidence for the reduction in symmetry of the sulphate anion from Td to C2v or even lower symmetry. The Raman band at 3629 cm−1 is assigned to the OH unit stretching vibration and the broad feature at around 3487 cm−1 to water stretching bands. Vibrational spectroscopy enables an assessment of the molecular structure of natural ettringite to be made.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

Choroidal thickness in childhood

Purpose To examine choroidal thickness (ChT) and its spatial distribution across the posterior pole in pediatric subjects with normal ocular health and minimal refractive error. Methods ChT was assessed using spectral domain optical coherence tomography (OCT) in 194 children aged between 4-12 years, with spherical equivalent refractive errors between +1.25 and -0.50 DS. A series of OCT scans were collected, imaging the choroid along 4 radial scan lines centered on the fovea (each separated by 45°). Frame averaging was used to reduce noise and enhance chorio-scleral junction visibility. The transverse scale of each scan was corrected to account for magnification effects associated with axial length. Two independent masked observers manually segmented the OCT images to determine ChT at foveal centre, and averaged across a series of perifoveal zones over the central 5 mm. Results The average subfoveal ChT was 330 ± 65 µm (range 189-538 µm), and was significantly influenced by age (p=0.04). The ChT of the 4 to 6 year old age group (312 ± 62 µm) was significantly thinner compared to the 7 to 9 year olds (337 ± 65 µm, p<0.05) and bordered on significance compared to the 10 to 12 year olds (341 ± 61 µm, p=0.08). ChT also exhibited significant variation across the posterior pole, being thicker in more central regions. The choroid was thinner nasally and inferiorly compared to temporally and superiorly. Multiple regression analysis revealed age, axial length and anterior chamber depth were significantly associated with subfoveal ChT (p<0.001). Conclusions ChT increases significantly from early childhood to adolescence. This appears to be a normal feature of childhood eye growth.

A reciprocal collaborative method using relevance feedback and feature importance

In a people-to-people matching systems, filtering is widely applied to find the most suitable matches. The results returned are either too many or only a few when the search is generic or specific respectively. The use of a sophisticated recommendation approach becomes necessary. Traditionally, the object of recommendation is the item which is inanimate. In online dating systems, reciprocal recommendation is required to suggest a partner only when the user and the recommended candidate both are satisfied. In this paper, an innovative reciprocal collaborative method is developed based on the idea of similarity and common neighbors, utilizing the information of relevance feedback and feature importance. Extensive experiments are carried out using data gathered from a real online dating service. Compared to benchmarking methods, our results show the proposed method can achieve noticeable better performance.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

Product feature taxonomy learning based on user reviews

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Integration of sentiment analysis into customer relational model : the importance of feature ontology and synonym

Online business or Electronic Commerce (EC) is getting popular among customers today, as a result large number of product reviews have been posted online by the customers. This information is very valuable not only for prospective customers to make decision on buying product but also for companies to gather information of customers’ satisfaction about their products. Opinion mining is used to capture customer reviews and separated this review into subjective expressions (sentiment word) and objective expressions (no sentiment word). This paper proposes a novel, multi-dimensional model for opinion mining, which integrates customers’ characteristics and their opinion about any products. The model captures subjective expression from product reviews and transfers to fact table before representing in multi-dimensions named as customers, products, time and location. Data warehouse techniques such as OLAP and Data Cubes were used to analyze opinionated sentences. A comprehensive way to calculate customers’ orientation on products’ features and attributes are presented in this paper.