22,011 research outputs found
Image analysis tools for the assessment of carbon anodes : a thesis presented in fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University
The energy efficiency and performance of an aluminium smelter depends critically on the quality and consistency of properties of the carbon electrodes that are consumed during the normal operation of the electrolytic cells or "pots". Unfortunately, although a small number of experts are able to assess anode quality by examining l0x images of samples, no objective method exists for making quality determinations. This thesis is about a project that has the goal of developing such an objective method. This thesis describes methods that have been developed for the characterization of the microstructure of carbon anodes. As a result of the process by which they are manufactured, carbon anodes contain pores or voids caused by out-gasing. In this continuing project we have concentrated on developing means for characterising the size and spatial distributions of these voids. Some of the methods used to characterise the spatial distribution include order neighbour analysis (a method used in geographical studies), and statistical texture analysis. These methods and the analysis described in this thesis are of general application
Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms
Currently, diagnosis of skin diseases is based primarily on visual pattern
recognition skills and expertise of the physician observing the lesion. Even
though dermatologists are trained to recognize patterns of morphology, it is
still a subjective visual assessment. Tools for automated pattern recognition
can provide objective information to support clinical decision-making.
Noninvasive skin imaging techniques provide complementary information to the
clinician. In recent years, optical coherence tomography has become a powerful
skin imaging technique. According to specific functional needs, skin
architecture varies across different parts of the body, as do the textural
characteristics in OCT images. There is, therefore, a critical need to
systematically analyze OCT images from different body sites, to identify their
significant qualitative and quantitative differences. Sixty-three optical and
textural features extracted from OCT images of healthy and diseased skin are
analyzed and in conjunction with decision-theoretic approaches used to create
computational models of the diseases. We demonstrate that these models provide
objective information to the clinician to assist in the diagnosis of
abnormalities of cutaneous microstructure, and hence, aid in the determination
of treatment. Specifically, we demonstrate the performance of this methodology
on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)
from healthy tissue
IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution
Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image- based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT), IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting image, run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases (LDB) algorithm in combination with Fisher’s criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%
Texture-based crowd detection and localisation
This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
and CT images of 300 patients from four different cohorts were analyzed for the
risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
head-and-neck cancer. Prediction models combining radiomic and clinical
variables were constructed via random forests and imbalance-adjustment
strategies using two of the four cohorts. Independent validation of the
prediction and prognostic performance of the models was carried out on the
other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
potential of radiomics for assessing the risk of specific tumour outcomes using
multiple stratification groups. This could have important clinical impact,
notably by allowing for a better personalization of chemo-radiation treatments
for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7
figures, 8 table
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