Implementing textural features on GPUs for improved real-time pavement distress detection

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics

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

TEXTURAL CLASSIFICATION OF MULTIPLE SCLEROSISLESIONS IN MULTIMODAL MRI VOLUMES

Background and objectives:Multiple Sclerosis is a common relapsing demyelinating diseasecausing the significant degradation of cognitive and motor skills and contributes towards areduced life expectancy of 5 to 10 years. The identification of Multiple Sclerosis Lesionsat early stages of a patient’s life can play a significant role in the diagnosis, treatment andprognosis for that individual. In recent years the process of disease detection has been aidedthrough the implementation of radiomic pipelines for texture extraction and classificationutilising Computer Vision and Machine Learning techniques. Eight Multiple Sclerosis Patient datasets have been supplied, each containing one standardclinical T2 MRI sequence and four diffusion-weighted sequences (T2, FA, ADC, AD, RD).This work proposes a Multimodal Multiple Sclerosis Lesion segmentation methodology util-ising supervised texture analysis, feature selection and classification. Three Machine Learningmodels were applied to Multimodal MRI data and tested using unseen patient datasets to eval-uate the classification performance of various extracted features, feature selection algorithmsand classifiers to MRI volumes uncommonly applied to MS Lesion detection. Method: First Order Statistics, Haralick Texture Features, Gray-Level Run-Lengths, His-togram of Oriented Gradients and Local Binary Patterns were extracted from MRI volumeswhich were minimally pre-processed using a skull stripping and background removal algorithm.mRMR and LASSO feature selection algorithms were applied to identify a subset of rankingsfor use in Machine Learning using Support Vector Machine, Random Forests and ExtremeLearning Machine classification. Results: ELM achieved a top slice classification accuracy of 85% while SVM achieved 79%and RF 78%. It was found that combining information from all MRI sequences increased theclassification performance when analysing unseen T2 scans in almost all cases. LASSO andmRMR feature selection methods failed to increase accuracy, and the highest-scoring groupof features were Haralick Texture Features, derived from Grey-Level Co-occurrence matrices

Transform texture classification

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1996.Includes bibliographical references (leaves 155-163).by Xiaoou Tang.Ph.D