Current Medical Imaging - Volume 15, Issue 7, 2019

Background: Gynaecological cancers (GCCa) are common and have a significant mortality rate all over the world. Early diagnosis of cancer can play a key role in the treatment and survival of a patient. Identification, staging, treatment, and monitoring of gynaecological malignancies is being done successfully by nuclear medicines. Discussion: Currently, single-photon emission computed tomography (SPECT) and positron emission tomography (PET) centered imaging techniques are being developed for use in patients with GCCa as a diagnostic tool. The present work elucidates several clinical studies on the use of radiopharmaceuticals, based on their effectiveness, in the early detection and management of GCCa. It also highlights the importance of reconsidering the biology for nuclear imaging as a future modality for early, rapid and efficient diagnosis of gynecological cancers. This comprehensive review is a part of our study designed to detect gynaecological cancers at an early stage using radionuclide complex, 99m Tc-Cisplatin. Conclusion: This article summarizes the significance of radioscintigraphy such as single-photon emission computed tomography (SPECT) and PET for identification of GCCa in the experimental humans and animals.

Background and Objective: Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems. Methods: Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected. Results: This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above. Conclusions: The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.

Purpose: To investigate the biomechanical properties of the optic nerve head in patients with Pseudoexfoliation (PEX) glaucoma using Real-time Elastography (RTE) and to compare these results with those of Primary Open Angle Glaucoma (POAG) patients and healthy subjects. Methods: Twenty eyes of 20 PEX glaucoma patients (PEX group), 20 eyes of 20 POAG patients (POAG group), and 20 eyes of 20 healthy subjects (control group) were enrolled in this prospective study. The strain Ratios of Orbital Fat to Optic Nerve head (ROFON) and lateral rectus muscle to optic nerve head (RLRON) were determined. Comparisons were performed using Chi-square, Kruskal Wallis, Mann-Whitney U, and One-way ANOVA tests. Results: The strain ratios of orbital fat to optic nerve head were 2.34, 6.85 and 1.76 in PEX glaucoma, POAG, and control groups, respectively (p<0.001). The strain ratios of the lateral rectus muscle to the optic nerve head were 0.51, 0.82, and 0.55 in PEX glaucoma, POAG, and control groups, respectively (p=0.256). Conclusion: The strain ratios of orbital fat to optic nerve head were different in PEX glaucoma patients than in POAG and control groups. RTE can provide biomechanical assessment of the optic nerve head in a non-invasive, quick, easily accessible, and user-friendly manner.

Objective: To validate the accuracy of the mandibular canal region in 3D biomodel produced by using data obtained from Cone-Beam Computed Tomography (CBCT) of cadaveric mandibles. Methods: Six hemi-mandible samples were scanned using the i-CAT CBCT system. The scanned data was transferred to the OsiriX software for measurement protocol and subsequently into Mimics software to fabricate customized cutting jigs and 3D biomodels based on rapid prototyping technology. The hemi-mandibles were segmented into 5 dentoalveolar blocks using the customized jigs. Digital calliper was used to measure six distances surrounding the mandibular canal on each section. The same distances were measured on the corresponding cross-sectional OsiriX images and the 3D biomodels of each dentoalveolar block. Results: Statistically no significant difference was found when measurements from OsiriX images and 3D biomodels were compared to the “gold standard” -direct digital calliper measurement of the cadaveric dentoalveolar blocks. Moreover, the mean value difference of the various measurements between the different study components was also minimal. Conclusion: Various distances surrounding the mandibular canal from 3D biomodels produced from the CBCT scanned data was similar to that of direct digital calliper measurements of the cadaveric specimens.

Background: Echocardiography and Cardiac Magnetic Resonance Imaging (CMRI) are two noninvasive techniques for the evaluation of cardiac function for patients with coronary artery diseases. Although echocardiography is the commonly used technique in clinical practice for the assessment of cardiac function, the measurement of LV volumes and left ventricular ejection fraction (LVEF) by the use of this technique is still influenced by several factors inherent to the protocol acquisition, which may affect the accuracy of echocardiography in the measurement of global LV parameters. Objective: The aim of this study is to compare the end systolic volume (ESV), the end diastolic volume (EDV), and the LVEF values obtained with three dimensional echocardiography (3D echo) with those obtained by CMRI (3 Tesla) in order to estimate the accuracy of 3D echo in the assessment of cardiac function. Methods: 20 subjects, (9 controls, 6 with myocardial infarction, and 5 with myocarditis) with age varying from 18 to 58, underwent 3D echo and CMRI. LV volumes and LVEF were computed from CMRI using a stack of cine MRI images in a short axis view. The same parameters were calculated using the 3D echo. A linear regression analysis and Bland Altman diagrams were performed to evaluate the correlation and the degree of agreement between the measurements obtained by the two methods. Results: The obtained results show a strong correlation between the 3D echo and CMR in the measurement of functional parameters (r = 0.96 for LVEF values, r = 0.99 for ESV and r= 0.98 for EDV, p < 0.01 for all) with a little lower values of LV volumes and higher values of LVEF by 3D echo compared to CMRI. According to statistical analysis, there is a slight discrepancy between the measurements obtained by the two methods. Conclusion: 3D echo represents an accurate noninvasive tool for the assessment of cardiac function. However, other studies should be conducted on a larger population including some complicated diagnostic cases.

Background: The aim of this study was to test a relational database including clinical data and imaging findings in a large cohort of subjects with suspected or known Coronary Artery Disease (CAD) undergoing stress single-photon emission computed tomography (SPECT) myocardial perfusion imaging. Methods: We developed a relational database including clinical and imaging data of 7995 subjects with suspected or known CAD. The software system was implemented by PostgreSQL 9.2, an open source object-relational database, and managed from remote by pgAdmin III. Data were arranged according to a logic of aggregation and stored in a schema with twelve tables. Statistical software was connected to the database directly downloading data from server to local personal computer. Results: There was no problem or anomaly for database implementation and user connections to the database. The epidemiological analysis performed on data stored in the database demonstrated abnormal SPECT findings in 46% of male subjects and 19% of female subjects. Imaging findings suggest that the use of SPECT imaging in our laboratory is appropriate. Conclusion: The development of a relational database provides a free software tool for the storage and management of data in line with the current standard.

Background: Scanning Electron Microscope (SEM) Camera Imaging shows and helps analyze hidden organs in the human body. SEM image analysis provides in-depth and critical details of organ abnormalities. Similarly, the human tongue finds use in the detection of organ dysfunction with tongue reflexology. Objective: To detect diabetes at an early stage using a non-invasive method of diabetes detection through tongue images and to utilize the reasonable cost of modality (SEM camera) for capturing the tongue images instead of the existing and expensive imaging modalities like X-ray, Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, Single-Photon Emission Computed Tomography etc. Methods: The tongue image is captured via SEM camera, it is preprocessed to remove noise and resize the tongue such that it is suitable for segmentation. Greedy Snake Algorithm (GSA) is used to segment the tongue image. The texture features of the tongue are analyzed and finally it is classified as diabetic or normal. Results: Failure of organs stomach, intestine, liver and pancreas results in change of the color of the tongue, coating thickness and cracks on the tongue. Changes in pancreas proactive behavior also reflect on tongue coating. The tongue coating texture varies from white or vanilla to yellow also the tongue coating thickness also increases. Conclusion: In this paper, the author proposes to diagnose Diabetes Type2 (DT2) at an early stage from tongue digital image. The tongue image is acquired and processed with Greedy Snake Algorithm (GSA) to extract edge and texture features.

Background: An approach based on QR decomposition, to remove speckle noise from medical ultrasound images, is presented in this paper. Methods: The speckle noisy image is segmented into small overlapping blocks. A global covariance matrix is calculated by averaging the corresponding covariances of the blocks. QR decomposition is applied to the global covariance matrix. To filter out speckle noise, the first subset of orthogonal vectors of the Q matrix is projected onto the signal subspace. The proposed approach is compared with five benchmark techniques; Homomorphic Wavelet Despeckling (HWDS), Speckle Reducing Anisotropic Diffusion (SRAD), Frost, Kuan and Probabilistic Non-Local Mean (PNLM). Results and Conclusion: When applied to different simulated and real ultrasound images, the QR based approach has secured maximum despeckling performance while maintaining optimal resolution and edge detection, and that is regardless of image size or nature of speckle; fine or rough.

Background: We propose a classification method for Alzheimer’s disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD. Methods: We obtained magnetic resonance images (MRIs) of Alzheimer’s patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This dataset consists of image data for AD, mild cognitive impairment (MCI), and normal controls (NCs), classified according to the cognitive condition. In this study, the research methods included image processing, texture analyses, and deep learning. Firstly, images were acquired for texture analyses, which were then re-spaced, registered, and cropped with Gabor filters applied to the resulting image data. In the texture analyses, we applied the 3-dimensional (3D) gray-level co-occurrence (GLCM) method to evaluate the textural features of the image, and used Fisher’s coefficient to select the appropriate features for classification. In the last stage, we implemented a deep learning multi-layer perceptron (MLP) model, which we divided into three types, namely, AD-MCI, AD-NC, and MCI-NC. Results: We used this model to assess the accuracy of the proposed method. The classification accuracy of the proposed deep learning model was confirmed in the cases of AD-MCI (72.5%), ADNC (85%), and MCI-NC (75%). We also evaluated the results obtained using a confusion matrix, support vector machine (SVM), and K-nearest neighbor (KNN) classifier and analyzed the results to objectively verify our model. We obtained the highest accuracy of 85% in the AD-NC. Conclusion: The proposed model was at least 6–19% more accurate than the SVM and KNN classifiers, respectively. Hence, this study confirms the validity and superiority of the proposed method, which can be used as a diagnostic tool for early Alzheimer’s diagnosis.

Background: In this study, we investigated the effect of hippocampal subfield atrophy on the development of Alzheimer’s disease (AD) by analyzing baseline magnetic resonance images (MRI) and images collected over a one-year follow-up period. Previous studies have suggested that morphological changes to the hippocampus are involved in both normal ageing and the development of AD. The volume of the hippocampus is an authentic imaging biomarker for AD. However, the diverse relationship of anatomical and complex functional connectivity between different subfields implies that neurodegenerative disease could lead to differences between the atrophy rates of subfields. Therefore, morphometric measurements at subfield-level could provide stronger biomarkers. Methods: Hippocampal subfield atrophies are measured using MRI scans, taken at multiple time points, and shape-based normalization to a Montreal neurological institute (MNI) ICBM 152 nonlinear atlas. Ninety subjects were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and divided equally into Healthy Controls (HC), AD, and mild cognitive impairment (MCI) groups. These subjects underwent serial MRI studies at three time-points: baseline, 6 months and 12 months. Results: We analyzed the subfield-level hippocampal morphometric effects of normal ageing and AD based on radial distance mapping and volume measurements. We identified a general trend and observed the largest hippocampal subfield atrophies in the AD group. Atrophy of the bilateral CA1, CA2- CA4 and subiculum subfields was higher in the case of AD than in MCI and HC. We observed the highest rate of reduction in the total volume of the hippocampus, especially in the CA1 and subiculum regions, in the case of MCI. Conclusion: Our findings show that hippocampal subfield atrophy varies among the three study groups.