MR Imaging Texture Analysis in the Abdomen and Pelvis

Texture analysis (TA) is a form of radiomics and refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MRI of the abdomen and pelvis, with the main strength being quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MR texture analysis (MRTA) including a dependency on image acquisition and reconstruction parameters, non-standardized approaches without or with image filtration, diverse software methods and applications, and statistical challenges relating numerous texture analysis results to clinical outcomes in retrospective pilot studies with small sample sizes. Despite these limitations, there is a growing body of literature supporting MRTA. In this review, the application of MRTA to the abdomen and pelvis will be discussed, including tissue or tumor characterization and response evaluation or prediction of outcomes in various tumors

Reduced reference image and video quality assessments: review of methods

With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications

Characterization of coarse aggregate angularity using digital image processing

This thesis involves a comparative analysis between well-established physically performed measurements for coarse aggregate and an image-based system. A thorough literature review was conducted, focusing on the importance of aggregate shape and how it is measured. In addition, state of the art in image-based aggregate measurement was reviewed along with the impacts and difficulties of an image-based methodology. Physical testing, consisting of Uncompacted Void Content, Index of Particle Shape and Texture (Compacted Voids), and Percent of Fractured Particles (fractured face or crush counts), was performed on aggregate control samples prior to testing on bulk aggregate samples. All aggregate samples were then tested with the WipShape imaging system to obtain Minimum Average Curve Radius measurement data. Finally, the results of physical testing and Minimum Average Curve Radius measurements were analyzed to determine if correlations exist between the different methods --Introduction, page 2-3

Reduced reference image and video quality assessments: review of methods

With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications

Patch Autocorrelation Features: A translation and rotation invariant approach for image classification.

The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods

Deep learning for texture and dynamic texture analysis

Texture is a fundamental visual cue in computer vision which provides useful information about image regions. Dynamic Texture (DT) extends the analysis of texture to sequences of moving scenes. Classic approaches to texture and DT analysis are based on shallow hand-crafted descriptors including local binary patterns and filter banks. Deep learning and in particular Convolutional Neural Networks (CNNs) have significantly contributed to the field of computer vision in the last decade. These biologically inspired networks trained with powerful algorithms have largely improved the state of the art in various tasks such as digit, object and face recognition. This thesis explores the use of CNNs in texture and DT analysis, replacing classic hand-crafted filters by deep trainable filters. An introduction to deep learning is provided in the thesis as well as a thorough review of texture and DT analysis methods. While CNNs present interesting features for the analysis of textures such as a dense extraction of filter responses trained end to end, the deepest layers used in the decision rules commonly learn to detect large shapes and image layout instead of local texture patterns. A CNN architecture is therefore adapted to textures by using an orderless pooling of intermediate layers to discard the overall shape analysis, resulting in a reduced computational cost and improved accuracy. An application to biomedical texture images is proposed in which large tissue images are tiled and combined in a recognition scheme. An approach is also proposed for DT recognition using the developed CNNs on three orthogonal planes to combine spatial and temporal analysis. Finally, a fully convolutional network is adapted to texture segmentation based on the same idea of discarding the overall shape and by combining local shallow features with larger and deeper features