The performance of solarscan : an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma

Objective To describe the diagnostic performance of SolarScan (Polartechnics Ltd, Sydney, Australia), an automated instrument for the diagnosis of primary melanoma.

Design Images from a data set of 2430 lesions (382 were melanomas; median Breslow thickness, 0.36 mm) were divided into a training set and an independent test set at a ratio of approximately 2:1. A diagnostic algorithm (absolute diagnosis of melanoma vs benign lesion and estimated probability of melanoma) was developed and its performance described on the test set. High-quality clinical and dermoscopy images with a detailed patient history for 78 lesions (13 of which were melanomas) from the test set were given to various clinicians to compare their diagnostic accuracy with that of SolarScan.

Setting Seven specialist referral centers and 2 general practice skin cancer clinics from 3 continents. Comparison between clinician diagnosis and SolarScan diagnosis was by 3 dermoscopy experts, 4 dermatologists, 3 trainee dermatologists, and 3 general practitioners.

Patients Images of the melanocytic lesions were obtained from patients who required either excision or digital monitoring to exclude malignancy.

Main Outcome Measures Sensitivity, specificity, the area under the receiver operator characteristic curve, median probability for the diagnosis of melanoma, a direct comparison of SolarScan with diagnoses performed by humans, and interinstrument and intrainstrument reproducibility.

Results The melanocytic-only diagnostic model was highly reproducible in the test set and gave a sensitivity of 91% (95% confidence interval [CI], 86%-96%) and specificity of 68% (95% CI, 64%-72%) for melanoma. SolarScan had comparable or superior sensitivity and specificity (85% vs 65%) compared with those of experts (90% vs 59%), dermatologists (81% vs 60%), trainees (85% vs 36%; P =.06), and general practitioners (62% vs 63%). The intraclass correlation coefficient of intrainstrument repeatability was 0.86 (95% CI, 0.83-0.88), indicating an excellent repeatability. There was no significant interinstrument variation (P = .80).

Conclusions SolarScan is a robust diagnostic instrument for pigmented or partially pigmented melanocytic lesions of the skin. Preliminary data suggest that its performance is comparable or superior to that of a range of clinician groups. However, these findings should be confirmed in a formal clinical trial.

3-dimensional characterization of membrane with nanoporous structure using TEM tomography and image analysis

The nanoporous structure of membrane varies in 3-dimensional (3-D) space and has remarkable influences on the filtration or desalination achieved, fouling potentials and therefore, the quality of yielded water. Knowledge of the 3-D nanoporous structure is thus vital to understanding and predicting its performance. A novel method by incorporating transmission electronic microtomography, image processing and 3-D reconstruction is introduced to characterize membranes with nano structures. The reconstruction algorithm allows for the visualization of 3-D nanoporous structure in a non-destructive way from any directions. This novel technique Ieads to in-depth understanding and accurate prediction of filtration performance.

3-dimensional characterization of membrane with nanoporous structure using TEM tomography and image analysis

The nanoporous structure of a membrane varies in a 3-dimensional (3-D) space and has remarkable influences on the filtration or desalination achieved, fouling potentials and therefore, the quality of yielded water. Knowledge of the 3-D nanoporous structure is thus vital to understanding and predicting its performance. A novel method by incorporating transmission electronic microtomography, image processing and 3-D reconstruction is introduced to characterize membranes with nano structures. The reconstruction algorithm allows for the visualization of 3-D nanoporous structure in a non-destructive way from any directions. This novel technique leads to in-depth understanding and accurate prediction of filtration performance.

Facial image analysis

This thesis focuses on novel technologies for facial image analysis, which involves three topics: face recognition under uncontrolled conditions, automatic facial age estimation, and context-aware fusion of face and gait. They are either key issues bridging laboratorial research and real applications, or innovative problems that have barely been studied before.

Computer-assisted image analysis of liver collagen: relationship to Ishak scoring and hepatic venous pressure gradient

Histopathological scoring of disease stage uses descriptive categories without measuring the amount of fibrosis. Collagen, the major component of fibrous tissue, can be quantified by computer-assisted digital image analysis (DIA) using histological sections. We determined relationships between DIA, Ishak stage, and hepatic venous pressure gradient (HVPG) reflecting severity of fibrosis. One hundred fifteen patients with hepatitis C virus (HCV) who had undergone transplantation had 250 consecutive transjugular liver biopsies combined with HVPG (median length, 22 mm; median total portal tracts, 12), evaluated using the Ishak system and stained with Sirus red for DIA. Liver collagen was expressed as collagen proportionate area (CPA). Median CPA was 6% (0.2-45), correlating with Ishak stage (stage 6 range, 13%-45%), and with HVPG (r = 0.62; P < 0.001). Median CPA was 4.1% when HVPG was less than 6 mm Hg and 13.8% when HVPG was 6 mm Hg or more (P < 0.0001) and 6% when HVPG was less than 10 mm Hg and 17.3% when HVPG was 10 mm Hg or higher (P < 0.0001). Only CPA, not Ishak stage/grade, was independently associated by logistic regression, with HVPG of 6 mm Hg or more [odds ratio, 1.206; 95% confidence interval (CI), 1.094-1.331; P < 0.001], or HVPG of 10 mm Hg or more (odds ratio, 1.105; 95% CI, 1.026-1.191; P = 0.009). CPA increased by 50% (3.6%) compared with 20% in HVPG (1 mm Hg) in 38 patients with repeated biopsies. Conclusion: CPA assessed by DIA correlated with Ishak stage scores and HVPG measured contemporaneously. CPA was a better histological correlate with HVPG than Ishak stage, had a greater numerical change when HVPG was low, and resulted in further quantitation of fibrosis in cirrhosis.

Haptics enabled offline AFM image analysis

Current advancements in nanotechnology are dependent on the capabilities that can enable nano-scientists to extend their eyes and hands into the nano-world. For this purpose, a haptics (devices capable of recreating tactile or force sensations) based system for AFM (Atomic Force Microscope) is proposed. The system enables the nano-scientists to touch and feel the sample surfaces, viewed through AFM, in order to provide them with better understanding of the physical properties of the surface, such as roughness, stiffness and shape of molecular architecture. At this stage, the proposed work uses of ine images produced using AFM and perform image analysis to create virtual surfaces suitable for haptics force analysis. The research work is in the process of extension from of ine to online process where interaction will be done directly on the material surface for realistic analysis.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Qualitative assessment of bubble behaviour for central and asymmetric injection in 2D gas-solid fluidized bed using image analysis technique and CFD modelling

Bubble characteristics such as shape, size, and trajectory control the hydrodynamics and therefore heat transfer in fluidized bed reactors. Thus understanding these characteristics is very important for the design and scaleup of fluidized beds. An earlier developed Eulerian-Eulerian two-fluid model for simulating dense gas–solid two-phase flow has been used to compare the experimental data in a pseudo-two-dimensional (2-D) bed. Bubbles are injected asymmetrically by locating the nozzle at proximity to the wall, thus presenting the effect wall has on asymmetrical injection as compared to symmetrical injection. In this work, a digital image analysis technique was developed to study the bubble behaviour in a two-dimensional bubbling bed. The high-speed photography reveals an asymmetric wake formation during detachment indicating an early onset of mixing process. The wall forces acts tangentially on thebubble and has a significant impact on the bubble shape, neck formation during detachment and its trajectory through the bed. Larger bubbles drifting away from the centre with longer paths are observed. This qualitative behaviour is well predicted by CFD modelling. Asymmetric injection can significantly influence the heat and mass transfer characteristics.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

Identification of animal fibers with wavelet texture analysis

This paper presents the use of the wavelet transform to extract fiber surface texture features for classifying cashmere and superfine merino wool fibers. Extracting features from brightness variations caused by the cuticular scale height, shape and interval provides an effective way for characterizing different animal fibers and subsequently classifying them. This may enable the development of a completely automated and objective system for animal fiber identification.

Automated film rhythm extraction for scene analysis

This paper examines film rhythm, an important expressive element in motion pictures, based on our ongoing study to exploit film grammar as a broad computational framework for the task of automated film and video understanding. Of the many, more or less elusive, narrative devices contributing to film rhythm, this paper discusses motion characteristics that form the basis of our analysis, and presents novel computational models for extracting rhythmic patterns induced through a perception of motion. In our rhythm model, motion behaviour is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighbourhoods in movies are then grouped by proportional makeup of these motion behavioural classes to yield seven high-level rhythmic arrangements that prove to be adept at indicating likely scene content (e.g. dialogue or chase sequence) in our experiments. Underlying causes for this level of codification in our approach are postulated from film grammar, and are accompanied by detailed demonstration from real movies for the purposes of clarification.