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Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%. [ABSTRACT FROM AUTHOR]

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization. [ABSTRACT FROM AUTHOR]

Purpose: The widely known field ‘Radiomics’ aims to provide an extensive image based phenotyping of e.g. tumors using a wide variety of feature values extracted from medical images. Therefore, it is of utmost importance that feature values calculated by different institutes follow the same feature definitions. For this purpose, the imaging biomarker standardization initiative (IBSI) provides detailed mathematical feature descriptions, as well as (mathematical) test phantoms and corresponding reference feature values. We present here an easy to use radiomic feature calculator, RaCaT, which provides the calculation of a large number of radiomic features for all kind of medical images which are in compliance with the standard. Methods: The calculator is implemented in C++ and comes as a standalone executable. Therefore, it can be easily integrated in any programming language, but can also be called from the command line. No programming skills are required to use the calculator. The software architecture is highly modularized so that it is easily extendible. The user can also download the source code, adapt it if needed and build the calculator from source. The calculated feature values are compliant with the ones provided by the IBSI standard. Source code, example files for the software configuration, and documentation can be found online on GitHub (). Results: The comparison with the standard values shows that all calculated features as well as image preprocessing steps, comply with the IBSI standard. The performance is also demonstrated on clinical examples. Conclusions: The authors successfully implemented an easy to use Radiomics calculator that can be called from any programming language or from the command line. Image preprocessing and feature settings and calculations can be adjusted by the user. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of automated grading of invasive breast carcinoma through the encoding of histological images as VLAD (Vector of Locally Aggregated Descriptors) representations on the Grassmann manifold. The proposed method considers each image as a set of multidimensional spatially-evolving signals that can be efficiently modeled through a higher-order linear dynamical systems analysis. Subsequently, each H&E (Hematoxylin and Eosin) stained breast cancer histological image is represented as a cloud of points on the Grassmann manifold, while a vector representation approach is applied aiming to aggregate the Grassmannian points based on a locality criterion on the manifold. To evaluate the efficiency of the proposed methodology, two datasets with different characteristics were used. More specifically, we created a new medium-sized dataset consisting of 300 annotated images (collected from 21 patients) of grades 1, 2 and 3, while we also provide experimental results using a large dataset, namely BreaKHis, containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results have shown that the proposed method outperforms a number of state of the art approaches providing average classification rates of 95.8% and 91.38% with our dataset and the BreaKHis dataset, respectively. [ABSTRACT FROM AUTHOR]

A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis. [ABSTRACT FROM AUTHOR]

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems. [ABSTRACT FROM AUTHOR]

Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly () better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms. [ABSTRACT FROM AUTHOR]