Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.

Spondyloarthritis (SpA) is a group of diseases primarily involving chronic inflammation of the spine and peripheral joints, as evaluated by magnetic resonance imaging (MRI). Considering the complexity of SpA, we performed a retrospective study to discover quantitative/radiomic MRI-based features correlated with SpA. We also investigated different fat-suppression MRI techniques to develop detection models for inflammatory sacroiliitis.

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task.

Manual and semiautomatic segmentation of bone sarcomas on MRI have high similarity.

The aims of this study were to evaluate the intra- and interobserver reproducibility of manual segmentation of bone sarcomas in magnetic resonance imaging (MRI) studies and to compare manual and semiautomatic segmentation methods. This retrospective study included twelve osteosarcoma and eight Ewing sarcoma MRI studies performed prior to any therapeutic intervention. All cases were histopathologically confirmed.

Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases.

Unlabelled: Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration BACKGROUND:  Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods.

Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity.

Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants.

Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures.

Background And Objective: Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint.

Methods: The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint.

Computer-aided diagnosis of plus disease in retinal fundus images of preterm infants via measurement of vessel tortuosity.

An increase in retinal vessel tortuosity can be indicative of the presence of various diseases including retinopathy of prematurity (ROP). Accurate detection and measurement of such changes could help in computer-aided diagnosis of plus disease, which warrants treatment of ROP. We present image processing methods for detection and segmentation of retinal vessels, quantification of vessel tortuosity, and diagnostic-decision-making criteria that incorporate the clinical definition of plus-diagnosis.

Classification of vertebral compression fractures in magnetic resonance images using spectral and fractal analysis.

Fractures with partial collapse of vertebral bodies are generically referred to as "vertebral compression fractures" or VCFs. VCFs can have different etiologies comprising trauma, bone failure related to osteoporosis, or metastatic cancer affecting bone. VCFs related to osteoporosis (benign fractures) and to cancer (malignant fractures) are commonly found in the elderly population.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue.

Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms.

This paper presents a review of recent advances in the development of methods for segmentation of the breast boundary and the pectoral muscle in mammograms. Regardless of improvement of imaging technology, accurate segmentation of the breast boundary and detection of the pectoral muscle are still challenging tasks for image processing algorithms. In this paper, we discuss problems related to mammographic image preprocessing and accurate segmentation.

Computer-aided diagnosis of plus disease via measurement of vessel thickness in retinal fundus images of preterm infants.

Changes in the characteristics of retinal vessels such as width and tortuosity can be signs of the presence of several diseases such retinopathy of prematurity (ROP) and diabetic retinopathy. Plus disease is an indicator of ROP which requires treatment and is signified by an increase in posterior venular width. In this work, we present image processing techniques for the detection, segmentation, tracking, and measurement of the width of the major temporal arcade (MTA), which is the thickest venular branch in the retina.

Assessment of vessel tortuosity in retinal images of preterm infants.

Diagnosis of plus disease is crucial for timely treatment and management of retinopathy of prematurity. An indicator of the presence of plus disease is an increase in the tortuosity of blood vessels in the retina. In this work, we propose a new angle-variation-based measure for quantification of tortuosity in retinal fundus images of preterm infants.

Development and validation of a fully automated system for detection and diagnosis of mammographic lesions.

We present a comprehensive and fully automated system for computer-aided detection and diagnosis of masses in mammograms. Novel methods for detection include: selection of suspicious focal areas based on analysis of the gradient vector field, rejection of oriented components of breast tissue using multidirectional Gabor filtering, and use of differential features for rejection of false positives (FPs) via clustering of the surrounding fibroglandular tissue. The diagnosis step is based on extraction of contour-independent features for characterization of lesions as benign or malignant from automatically detected circular and annular regions.

Analysis of structural similarity in mammograms for detection of bilateral asymmetry.

We hypothesize that quantification of structural similarity or dissimilarity between paired mammographic regions can be effective in detecting asymmetric signs of breast cancer. Bilateral masking procedures are applied for this purpose by using automatically detected anatomical landmarks. Changes in structural information of the extracted regions are investigated using spherical semivariogram descriptors and correlation-based structural similarity indices in the spatial and complex wavelet domains.

Quantification of the changes in the openness of the major temporal arcade in retinal fundus images of preterm infants with plus disease.

Purpose: We tested the hypothesis that the openness of the major temporal arcade (MTA) changes in the presence of plus disease, by quantification via parabolic modeling of the MTA, as well as measurement of an arcade angle for comparative analysis. Such analysis could assist in the detection and treatment of progressive retinopathy of prematurity.

Methods: Digital image processing techniques were applied for the detection and modeling of the MTA via a graphical user interface (GUI) to quantify the openness of the MTA.

Estimation of the breast skin-line in mammograms using multidirectional Gabor filters.

Segmentation of the breast region is a fundamental step in any system for computerized analysis of mammograms. In this work, we propose a novel procedure for the estimation of the breast skin-line based upon multidirectional Gabor filtering. The method includes an adaptive values-of-interest (VOI) transformation, extraction of the skin-air ribbon by Otsu's thresholding method and the Euclidean distance transform, Gabor filtering with 18 real kernels, and a step for suppression of false edge points using the magnitude and phase responses of the filters.

Measures of radial correlation and trend for classification of breast masses in mammograms.

In this paper, a novel approach for classification of breast masses is presented that quantifies the texture of masses without relying on accurate extraction of their contours. Two novel feature descriptors based on 2D extensions of the reverse arrangement (RA) and Mantel's tests were designed for this purpose. Measures of radial correlation and radial trend were extracted from the original gray-scale values as well as from the Gabor magnitude response of 146 regions of interest, including 120 benign masses and 26 malignant tumors.

Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns.

Computer-aided diagnosis of proliferative diabetic retinopathy via modeling of the major temporal arcade in retinal fundus images.

Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate diagnosis and treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided semiautomated modeling and measurement of the openness of the MTA based on Gabor filters for the detection of retinal vessels, morphological image processing, and a form of the generalized Hough transform for the detection of parabolas. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 11 normal individuals and 11 patients with PDR in the present pilot study on potential clinical application.

Automatic detection of the nipple in screen-film and full-field digital mammograms using a novel Hessian-based method.

Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs).

Computer-aided diagnosis of proliferative diabetic retinopathy.

Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate improved diagnosis and timely treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided modeling and measurement of the openness of the MTA based on a form of the generalized Hough transform for the detection of parabolas, and to compare it with a method of arcade angle measurement. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 10 normal individuals and 15 patients with PDR.

Classification of color images of dermatological ulcers.

We present color image processing methods for the analysis of images of dermatological lesions. The focus of the present work is on the application of feature extraction and selection methods for classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yellow), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist into the classes mentioned above.

Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms.

Purpose: We propose a method for the detection of architectural distortion in prior mammograms of interval-cancer cases based on the expected orientation of breast tissue patterns in mammograms.

Methods: The expected orientation of the breast tissue at each pixel was derived by using automatically detected landmarks including the breast boundary, the nipple, and the pectoral muscle (in mediolateral-oblique views). We hypothesize that the presence of architectural distortion changes the normal expected orientation of breast tissue patterns in a mammographic image.