Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images
AbstractImage quality assessments (IQA) are an important task for providing appropriate medical care. Full-reference IQA (FR-IQA) methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are often used to evaluate imaging conditions, reconstruction conditions, and image processing algorithms, including noise reduction and super-resolution technology. However, these IQA methods may be inapplicable for medical images because they were designed for natural images. Therefore, this study aimed to investigate the correlation between objective assessment by some FR-IQA methods and human subjective asse...
Source: Journal of Digital Imaging - August 7, 2023 Category: Radiology Source Type: research
Subject-Specific Automatic Reconstruction of White Matter Tracts
AbstractMRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional –anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In t his work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test–retest reproducibil...
Source: Journal of Digital Imaging - August 3, 2023 Category: Radiology Source Type: research
PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI
This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity . Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyra...
Source: Journal of Digital Imaging - August 3, 2023 Category: Radiology Source Type: research
Subject-Specific Automatic Reconstruction of White Matter Tracts
AbstractMRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional –anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In t his work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test–retest reproducibil...
Source: Journal of Digital Imaging - August 3, 2023 Category: Radiology Source Type: research
PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI
This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity . Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyra...
Source: Journal of Digital Imaging - August 3, 2023 Category: Radiology Source Type: research
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis
AbstractBreast cancer is the second most common cancer among women worldwide, and the diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from the activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to higher accuracy in the classification task and dimension reductio...
Source: Journal of Digital Imaging - August 2, 2023 Category: Radiology Source Type: research
The Keratectasia Volume (KEV) in Corneal Topography to Evaluate the Effect of Corneal Collagen Cross-linking in Pediatric Keratoconus
This study included 40 eyes of 25 pediatric patients (10 –19 years) undergoing standard CXL. The support vector machine (SVM) algorithm was applied to transform mass pixels in corneal topography into a three-dimensioned model to calculate the KEV. The KEV, Kmax, K1, K2, Kave, keratectasia area (KEA), and thinnest corneal thickness (TCT) were determined before CXL and at 3, 6, and 12 months after surgery. The correlation between KEV and other parameters (Kmax, TCT, max decentration, eccentricity, and so on) was calculated. The KEV was 4.75 ± 0.74 preoperatively and 4.43 ± 1.22 postoperatively at last follow-up...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study
In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient ’s scan. A cr...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data
AbstractThe current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, “ChestX-ray14,” which involved fourteen (14) disease labels, was randomly divided into the train, validation, and test sets with ratios of 0.7, 0.1, and 0.2. Two pretrained state-of-the-art networks, EfficientNet-b5 and CoAtNet-0-rw, were chosen as the backbones. After the fully-connected layer, a final layer of 14 sigmoid activation units was added to output each disease’s diagnosis. To achieve b...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI
AbstractCurrently, obtaining accurate medical annotations requires high labor and time effort, which largely limits the development of supervised learning-based tumor detection tasks. In this work, we investigated a weakly supervised learning model for detecting breast lesions in dynamic contrast-enhanced MRI (DCE-MRI) with only image-level labels. Two hundred fifty-four normal and 398 abnormal cases with pathologically confirmed lesions were retrospectively enrolled into the breast dataset, which was divided into the training set (80%), validation set (10%), and testing set (10%) at the patient level. First, the second im...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction
AbstractMicroscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
Improving Accuracy of Pneumonia Classification Using Modified DenseNet
(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
