Texture-based Visualization of Metrics on Software Architectures

We present a method that combines textures, blending, and scattered-data interpolation to visualize several metrics defined on overlapping areas-of-interest on UML class diagrams. We aim to simplify the task of visually correlating the distribution and outlier values of a multivariate metric dataset with a system’s structure. We illustrate our method on a class diagram of a real-world system.

Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture

This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dissolve, and animal hybridization.Comment: Accepted to CVPR'1

A Quality of Recognition Case Study: Texture-based Segmentation and MRI Quality Assessment

Muscle texture may be used as a descriptive feature for the segmentation of skeletal muscle in Magnetic Resonance Images (MRI). However, MRI acquisition is not always ideal and the texture richness might become compromised. Moreover, the research for the development of texture quality metrics, and particularly no-reference metrics, to be applied to the specific context of MRI is still in a very early stage. In this paper, a case study is established from a texture-based segmentation approach for skeletal muscle, which was tested in a thigh Dixon MRI database. Upon the obtained performance measures, the relation between objective image quality and the texture MRI richness is explored, considering a set of state-of-the-art no-reference image quality metrics. A discussion on the effectiveness of existing quality assessment methods in measuring MRI texture quality is carried out, based on Pearson and Spearman correlation outcomes.info:eu-repo/semantics/publishedVersio

Perceptual Image Similarity Metrics and Applications.

This dissertation presents research in perceptual image similarity metrics and applications, e.g., content-based image retrieval, perceptual image compression, image similarity assessment and texture analysis. The first part aims to design texture similarity metrics consistent with human perception. A new family of statistical texture similarity features, called Local Radius Index (LRI), and corresponding similarity metrics are proposed. Compared to state-of-the-art metrics in the STSIM family, LRI-based metrics achieve better texture retrieval performance with much less computation. When applied to the recently developed perceptual image coder, Matched Texture Coding (MTC), they enable similar performance while significantly accelerating encoding. Additionally, in photographic paper classification, LRI-based metrics also outperform pre-existing metrics. To fulfill the needs of texture classification and other applications, a rotation-invariant version of LRI, called Rotation-Invariant Local Radius Index (RI-LRI), is proposed. RI-LRI is also grayscale and illuminance insensitive. The corresponding similarity metric achieves texture classification accuracy comparable to state-of-the-art metrics. Moreover, its much lower dimensional feature vector requires substantially less computation and storage than other state-of-the-art texture features. The second part of the dissertation focuses on bilevel images, which are images whose pixels are either black or white. The contributions include new objective similarity metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of objective metrics. Several similarity metrics are proposed that outperform existing ones in the sense of attaining significantly higher Pearson and Spearman-rank correlations with the ground truth. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram, Connected Components Comparison and combinations of such. Another portion of the dissertation focuses on the aforementioned MTC, which is a block-based image coder that uses texture similarity metrics to decide if blocks of the image can be encoded by pointing to perceptually similar ones in the already coded region. The key to its success is an effective texture similarity metric, such as an LRI-based metric, and an effective search strategy. Compared to traditional image compression algorithms, e.g., JPEG, MTC achieves similar coding rate with higher reconstruction quality. And the advantage of MTC becomes larger as coding rate decreases.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113586/1/yhzhai_1.pd

Establishing Relevant ADC-based Texture Analysis Metrics for Quantifying Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinomas

Purpose: The purpose of this study is to identify which texture analysis metrics calculated from apparent diffusion coefficient (ADC) maps from patients with head and neck squamous cell carcinomas (HNSCC) provide quantifiable measures of tumor physiology changes. We discerned which imaging metrics were relevant using baseline agreement and variations during early treatment. Methods: For selective patients with stages II-IV HNSCC, ADC maps were generated from two baselines, taken 1 week apart, and one early treatment scan, obtained during the 2nd week of curative-intent chemoradiation therapy. Regions of interest (ROI), consisting of primary and nodal disease were drawn onto resampled ADC maps. Four 3D texture matrices describing local and regional relationships between voxel intensities in the ROIs were generated. From these, 38 texture metrics and 7 histogram features were calculated for each patient, including the mean and median ADC. Agreement between the two baseline measures was estimated with the intra-class correlation coefficient (ICC). For each metric with an ICC≥0.80, the Wilcoxon signed-rank test was used to test if the difference between the mean of the baselines and the early treatment was non-zero. Results: Texture analysis was implemented on nine patients that had both baselines and early treatment images. Due to baseline agreement, only 9 of the 45 metrics had an ICC ≥0.80, including ADC mean and median. Six of these 9 metrics had a p-value \u3c 0.05. Only 1 of the 9 metrics remained of interest, after applying the Holm correction to the alpha levels: the run length non-uniformity metric (p = 0.004) in the Gray Level Run Length Matrix. Conclusion: The feasibility of texture analysis is dependent on the baseline agreement of each metric, which disqualifies many texture characteristics. However, metrics with high ICC have potential to provide additional quantitative information for the assessment of early treatment changes for HNSCC

Multiple Texture Boltzmann Machines

We assess the generative power of the mPoTmodel of [10] with tiled-convolutional weight sharing as a model for visual textures by specifically training on this task, evaluating model performance on texture synthesis and inpainting tasks using quantitative metrics. We also analyze the relative importance of the mean and covariance parts of the mPoT model by comparing its performance to those of its subcomponents, tiled-convolutional versions of the PoT/FoE and Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM). Our results suggest that while state-of-the-art or better performance can be achieved using the mPoT, similar performance can be achieved with the mean-only model. We then develop a model for multiple textures based on the GB-RBM, using a shared set of weights but texturespecific hidden unit biases. We show comparable performance of the multiple texture model to individually trained texture models.

Combined multi-protocols qMRI for thigh muscle analysis: a preliminary study

Quantitative MRI (qMRI) has been shown to be crucial for assessing organ dysfunction in the body. Usually, in qMRI approaches, a few metrics are extracted to distinguish normal and abnormal tissues. In this study, we coupled four MRI protocols (mDIXON T1, T1 and T2 mapping and DTI) to obtain 34 complementary metrics including 20 shape metrics, 2 texture metrics and 12 water diffusivity metrics for thigh muscle analysis. These metrics were calculated on both thighs to detect a pathological difference between a pair of right and left muscles. The method is based on a dimension reduction method and a projection of shape and diffusivity metrics into a three-dimensional linear latent space, along with two texture metrics. 5 healthy individuals (10 thighs, each thigh 7 muscles, i.e., 4 exors and 3 extensors) were scanned to provide the reference scores. The developed pipeline was used to analyse the pair thighs of 4 patients in order to suggest a specific muscle therapy before total knee arthroplasty (TKA) individually for each of the 7 muscles. Preliminary results from the analysis of thigh muscle texture, shape and diffusivity showed that this qMRI protocol can help to suggest a targeted, patient-specific exercise plan to improve muscle recovery after TKA surgery. More healthy and pathological subjects are needed to confirm these encouraging results