Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews
Podcast by Author(s): Euishin Edmund Kim.
Volume 15 , Issue 9 , 2019
Web: https://www.eurekaselect.com/174515/a…
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Current Medical Imaging publishes frontier review articles, original research articles, case reports, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques.
The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis. To know more about the Journal, please visit: https://benthamscience.com/journals/current-medical-imaging/
Aim: The aim of our study was to determine whether the diffusion properties of the auditory pathways alter between patients with Neurofibromatosis type 1 (NF1) and the healthy subjects. DTI can well demonstrate FA and ADC changes in auditory tracts and it may be a guide to identify the candidates for hearing loss among NF1 children.
Methods: The study population consisted of 43 patients with NF1 and 21 healthy controls. Diffusion tensor imaging (DTI) was used to measure apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values from lemniscus lateralis, colliculus inferior, corpus geniculatum mediale and Heschl’s gyrus. The results were compared with those of the control group.
Results: The ADC values of lateral lemniscus, colliculus inferior and corpus geniculatum mediale were significantly higher in NF1 compared to those of the control group. On the other hand, decreased FA values were observed in lateral lemniscus and colliculus inferior in patients with NF1.
Conclusion: The increase in ADC and reduction in FA in the auditory pathways of patients with NF1 may suggest microstructural alterations, such as a decrease in the number of axons, edema or inflammation in the auditory tracts. To read out more, please visit: http://www.eurekaselect.com/161550/article
Author(s): Nuwan Madusanka, Heung-Kook Choi*, Jae-Hong So, Boo-Kyeong Choi.
Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD).
Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity.
Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance. To read out more, please visit: http://www.eurekaselect.com/166178/article
Author(s): Liu Xia, Liu Xiao, Gan Quan, Wang Bo*.
Background: Automatic approach to vertebrae segmentation from computed tomography (CT) images is very important in clinical applications. As the intricate appearance and variable architecture of vertebrae across the population, cognate constructions in close vicinity, pathology, and the interconnection between vertebrae and ribs, it is a challenge to propose a 3D automatic vertebrae CT image segmentation method.
Objective: The purpose of this study was to propose an automatic multi-vertebrae segmentation method for spinal CT images.
Methods: Firstly, CLAHE-Threshold-Expansion was preprocessed to improve image quality and reduce input voxel points. Then, 3D coarse segmentation fully convolutional network and cascaded finely segmentation convolutional neural network were used to complete multi-vertebrae segmentation and classification.
Results: The results of this paper were compared with the other methods on the same datasets. Experimental results demonstrated that the Dice similarity coefficient (DSC) in this paper is 94.84%, higher than the V-net and 3D U-net.
Conclusion: Method of this paper has certain advantages in automatically and accurately segmenting vertebrae regions of CT images. Due to the easy acquisition of spine CT images. It was proven to be more conducive to clinical application of treatment that uses our segmentation model to obtain vertebrae regions, combining with the subsequent 3D reconstruction and printing work. To read out more, please visit: http://www.eurekaselect.com/168045/article
Background: To evaluate the hysterosalpingography (HSG) findings of women with infertility in a tertiary center located in central Turkey.
Methods: A total of 1,996 patients undergoing the HSG procedure for the investigation of infertility from April 2012 to 2017 were retrospectively evaluated using the archives of the reproductive endocrinology and radiology departments. Demographic and clinical characteristics of patients with normal HSG findings (n = 1,549) and patients with abnormal HSG findings (n = 447) were compared, and the distribution of pathologies on the HSG examinations was evaluated as well.
Results: There were statistically significant differences between patients with normal and abnormal HSG findings in terms of age (25.68 ± 4.54 vs. 35.87 ± 2.65, p < 0.001), type (for secondary) and duration of infertility [43.1% vs. 50.6% (p = 0.006); 7 (1-22) vs. 2 (1-12) (p < 0.001), respectively], and baseline follicle stimulating hormone and estradiol levels [7.22 ± 1.38 vs. 7.55 ± 1.42 (p < 0.001); 45.54 ± 9.92 vs. 44.40 ± 9.99 (p < 0.001), respectively]. Among a total of 1,996 HSG examinations, 447 (22.39%) showed abnormalities, of which 237 (11.87%) were associated with tubal pathologies, 163 (8.17%) with uterine pathologies, and 47 (2.35%) with a combination of both. While the most common tubal pathology was one-sided distal tubal occlusion (2.91%), the most common uterine pathology was filling defects (4.16%).
Conclusion: HSG is the most commonly used, well-tolerated, low-cost, and safe radiological procedure to use for the investigation of the causes of female infertility. To read out more, please visit: http://www.eurekaselect.com/165406/article
Current Medical Imaging publishes frontier review articles, original research articles, case reports, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques.
The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis. To learn more about the journal, please visit: https://benthamscience.com/journals/current-medical-imaging/
This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach.
The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging. This survey identifies the ML approach with better accuracy for medical diagnosis by radiologists. The image denoising approaches further includes basic filtering techniques, wavelet medical denoising, curvelet and optimization techniques. In most of the applications, the machine learning performance is better than the conventional image denoising techniques. For fast and computational results the radiologists are using the machine learning methods on MRI, US, X-Ray and Skin lesion images. The characteristics and contributions of different ML approaches are considered in this paper. Read out full article here: http://www.eurekaselect.com/152027
Journal Name: Current Medical Imaging
In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD).
In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Read out full article here: http://www.eurekaselect.com/166178
Bentham Science 
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