Fast Semivariogram Computation Using FPGA Architectures

The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, Î(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O (n2). Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This thesis presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from MRI scans are utilized for the experiments. Implementation results of the first architecture shows that a significant advantage in computational speed is attained by the architectures with respect to Matlab implementation on a personal computer with an Intel i7 multi-core processor. It is also observed that the massively pipelined architecture is nearly 100 times faster than the non-pipelined architecture

Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN)

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results

Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture non-uniformities making their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough and expedited health monitoring of roads. In the pavement monitoring area, well known texture descriptors such as gray-level co-occurrence matrices and local binary patterns are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature

In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM).

We present our latest work on in vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). The in vivo skin capacitive images were taken by a capacitance based fingerprint sensor, the skin capacitive images were then analysed by GLCM. Four different GLCM feature vectors, angular second moment (ASM), entropy (ENT), contrast (CON) and correlation (COR), are selected to describe the skin texture. The results show that angular second moment increases as age increases, and entropy decreases as age increases. The results also suggest that the angular second moment values and the entropy values reflect more about the skin texture, whilst the contrast values and the correlation values reflect more about the topically applied solvents. The overall results shows that the GLCM is an effective way to extract and analyse the skin texture information, which can potentially be a valuable reference for evaluating effects of medical and cosmetic treatments

Detection of Myofascial Trigger Points With Ultrasound Imaging and Machine Learning

Myofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder that affects a large portion of the global population, seen in 85-93% of patients in specialty pain clinics [10]. MPS is characterized by hard, palpable nodules caused by a stiffened taut band of muscle fibers. These nodules are referred to as Myofascial Trigger Points (MTrPs) and can be classified by two states: active MTrPs (A-MTrPs) and latent MtrPs (L-MTrPs). Treatment for MPS involves massage therapy, acupuncture, and injections or painkillers. Given the subjectivity of patient pain quantification, MPS can often lead to mistreatment or drug misuse. A deterministic way to quantify the pain is needed for better diagnosis and treatment. Various medical imaging technologies have been used to try to find quantifiable and measurable biomarkers of MTrPs. Ultrasound imaging, with it’s accessibility and variety of modalities, has shown significant findings in identifying MTrPs. Elastography ultrasound, which is used for measuring stiffness in soft tissues, has shown that MTrPs tend to be stiffer than normal muscle tissue. Doppler ultrasound has shown that bloodflow velocities differ significantly in areas surrounding MTrPs. MTrPs have been identified in standard B-mode grayscale ultrasound, but have varying conclusions with some studies identifying them as dark hypoechoic blobs and other studies showing them as bright hyperechoic blobs. Despite these discoveries, there is a high variance among results with no correlations to severity or pain. As a step towards quantifying the pain associated with MTrPs, this work aims to introduce a machine learning approach using image processing with texture recognition to detect MTrPs in Bmode ultrasound. A texture recognition algorithm called Gray Level Co-Occurrence Matrix (GLCM) is used to extract texture features from the B-mode ultrasound image. Feature maps are generated to emphasize these texture features in an image format in anticipation that a deep convolutional neural network will be able to correlate the features with the presence of a MTrP. The GLCM feature maps are compared to the elastography ultrasound to determine any correlations with muscle stiffness and then evaluated in the presence of MTrPs. The feature map generation is accelerated with a GPU-based implementation for the goal of real-time processing and inference of the machine learning model. Finally, two deep learning models are implemented to detect MTrPs comparing the effect of using GLCM feature maps of B-mode ultrasound to emphasize texture features for machine learning model inputs

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.Comment: 8 pages, 16 figures, 11 table

A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement

Skin Hydration and Solvent Penetration Measurements by Opto-thermal Radiometry, AquaFlux and Fingerprint Sensor

The aim of this study is to develop new data analysis techniques and new measurement methodologies for skin hydration and solvent penetration measurements by using Opto-Thermal Transient Emission Radiometry (OTTER), AquaFlux and capacitive contact imaging based on Fingerprint sensor, three novel technologies developed by our research group. This research work is divided into three aspects: the theoretical work, the experimental work and the portable opto-thermal radiometry hardware design work. In the theoretical work, a) an effective image retrieval method based on Gabor wavelet transform has been developed, the results show that it is particularly useful for retrieving the grayscale capacitive skin images; b) an algorithm based on Grey Level Co-occurrence Matrix (GLCM) has been developed to analyze the grayscale capacitive skin images; c) a comparison study of Gabor wavelet transform, Grey level co-occurrence matrix (GLCM) and Principal Component Analysis (PCA) has been conducted in order to understand the performance of each algorithm, and to find out which algorithm is suitable for what type of images. In the opto-thermal radiometry hardware design work, a new, low cost, portable opto-thermal radiometry instrument, based on a broadband Infrared emitter and a room temperature PbS detector, has been designed and developed. The results show that it can work on any unprepared sample surfaces. In the experimental work, various in-vivo and in-vitro measurements were performed in order to study skin hydration and solvent penetration through skin and membranes. The results show that, combined with tape stripping, capacitive skin imaging can be a powerful tool for skin hydration, skin texture and solvent penetration measurements. The effect of three different parameters of Fingerprint sensor and its detection depth are also studied. The outcomes of this work have provided a better understanding for skin hydration and solvent penetration measurements and have generated several publications