Current Medical Imaging - Volume 15, Issue 2, 2019

Background: Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival. Discussion: This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection. Conclusion: This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Background: Early detection of breast cancer, combined with effective treatment, can reduce mortality. Millions of women are diagnosed with breast cancer and many die every year globally. Numerous early detection screening tests have been employed. A wide range of current breast cancer screening methods are reviewed based on a series of searchers focused on clinical testing and performance. Discussion: The key factors evaluated centre around the trade-offs between accuracy (sensitivity and specificity), operator dependence of results, invasiveness, comfort, time required, and cost. All of these factors affect the quality of the screen, access/eligibility, and/or compliance to screening programs by eligible women. This survey article provides an overview of the working principles, benefits, limitations, performance, and cost of current breast cancer detection techniques. It is based on an extensive literature review focusing on published works reporting the main performance, cost, and comfort/compliance metrics considered. Conclusion: Due to limitations and drawbacks of existing breast cancer screening methods there is a need for better screening methods. Emerging, non-invasive methods offer promise to mitigate the issues particularly around comfort/pain and radiation dose, which would improve compliance and enable all ages to be screened regularly. However, these methods must still undergo significant validation testing to prove they can provide realistic screening alternatives to the current accepted standards.

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

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.

Background: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum. Objective: The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS). Methods: In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach. Results: The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest. Conclusion: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.

Background: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms. Methods: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared. Results: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB. Conclusion: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Objective: The aim of the current study was to investigate a change in the volume of the frontal and maxillary sinuses in patients with nasal septum deviations due to physical trauma. Materials and Methods: Paranasal sinus computed tomography data of 100 patients admitted to Kirikkale University medical faculty hospital between November 2013 and June 2014 were retrospectively analyzed. The side of the nasal septal deviation, the deviation angle, the severity of the deviation, and bilateral frontal and maxillary sinus volumes were calculated using a computer program. The relationship between sinus volumes and deviated septum characteristics was investigated. Results: The maxillary sinus volumes did not differ between the two genders. However, the female patients had significantly decreased frontal sinus volumes when compared with the male patients (p < 0.05). A right-sided septal deviation was found to be associated with a significantly decreased maxillary sinus volume (p < 0.001), and the severity of the deviation was a significant determinant of the maxillary sinus volume (p < 0.001). The age of the patient at the time of the septal trauma was significantly associated with their maxillary sinus volumes. Patients who had experienced this trauma after 12 years of age had significantly increased maxillary sinus volumes when compared with those who experienced the trauma before the age of 12. Conclusion: A distorted septal anatomy was found to be a significant parameter for developing paranasal sinuses. Right-sided and severe traumatic deviations with an onset before the age of 12 were significantly associated with a decreased maxillary sinus volume.

Background: Masses are one of the most important indicators of breast cancer in mammograms, and their classification into two groups as benign and malignant is highly necessary. Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert. Methods: In this research, classification of breast masses using mammography in the two main views which include MLO and CC, is evaluated with respect to the shape, texture and asymmetry aspect. Additionally, a method was developed and proposed using the classification of breast tissue density based on the decision tree. Discussion: This study therefore, aims to provide a method based on the human decision-making model that will help in designing the perfect tool for radiologists, regardless of the complexity of computing, costly procedures and also reducing the diagnosis error. Conclusion: Results show that the proposed system for entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense breast achieved 100, 99, 99 and 98% true malignant rate, respectively with cross-validation procedure.

Objective: Accurate assessment of breast tumor size preoperatively is important for the initial decision-making in surgical approach. Therefore, we aimed to compare efficacy of mammography and ultrasonography in ductal carcinoma in situ (DCIS) of breast cancer. Methods: Preoperative mammography and ultrasonography were performed on 104 women with DCIS of breast cancer. We compared the accuracy of each of the imaging modalities with pathological size by Pearson correlation. For each modality, it was considered concordant if the difference between imaging assessment and pathological measurement is less than 0.5cm. Results: At pathological examination tumor size ranged from 0.4cm to 7.2cm in largest diameter. For mammographically determined size versus pathological size, correlation coefficient of r was 0.786 and for ultrasonography it was 0.651. Grouped by breast composition, in almost entirely fatty and scattered areas of fibroglandular dense breast, correlation coefficient of r was 0.790 for mammography and 0.678 for ultrasonography; in heterogeneously dense and extremely dense breast, correlation coefficient of r was 0.770 for mammography and 0.548 for ultrasonography. In microcalcification positive group, coeffient of r was 0.772 for mammography and 0.570 for ultrasonography. In microcalcification negative group, coeffient of r was 0.806 for mammography and 0.783 for ultrasonography. Conclusion: Mammography was more accurate than ultrasonography in measuring the largest cancer diameter in DCIS of breast cancer. The correlation coefficient improved in the group of almost entirely fatty/ scattered areas of fibroglandular dense breast or in microcalcification negative group.

Background: The assessment of cardiac wall motion abnormalities plays an important role in the evaluation of many cardiovascular diseases and the prediction of functional recovery. Most of the methods dedicated to identifying the location of wall motion abnormalities have been restricted to study hypokinesia while an accurate way to assess dyskinesia is still needed in Cardiac Magnetic Resonance Imaging (CMRI). Objective: The aim of this study is to propose a phase image based on the analytic signal able to assess the extent of the myocardial dyskinetic segments in Cardiac Magnetic Resonance Imaging (CMRI). Materials: 22 subjects were retrospectively enrolled in this study (age 46 ± 11): 15 presenting an aneurysm and 7 control subjects with normal wall motion. For each patient, three standard views (short axis view, 2 chamber and 4 chamber views) were acquired using 3 Tesla Siemens Avanto MRI scanner and a segmented True FISP sequence. All the cine MRI images were analyzed by two experimented observers who were blinded to the diagnostic results. Results: The outcomes of this study show that using the proposed phase image in MRI clinical routine can increase the accuracy of the detection of myocardial dyskinetic segments from 77.23 % to 86.38 %, the sensitivity from 67.48 % to 78.86 % as well the specificity from 80.92 % to 89.23 % compared to the standard method based on cine MRI interpretation. Conclusion: The phase image is a promising tool in CMRI for the assessment of dyskinetic segments and the degree of myocardial asynchronism.

Background: Chronic exposure to occupational ionising radiation is seen as one reason for elevated cancer prevalence. Objective: The aim of this retrospective study was to evaluate radiation exposure of anaesthetists by real-time dosimetry. Methods: Data of 296 patients were analyzed. Ten types of trauma operation procedures including osteosynthesis of upper and lower extremity fractures and minimally invasive stabilisation of traumatic and osteoporotic vertebral fractures were accomplished. Evaluation was performed by an occupational dosimetry system, which visualises anaesthetists radiation exposure feedback compared to surgeons in real-time. Results: A significantly lower radiation exposure to anaesthetists compared to surgeons was observed in four types of operative procedures: Plate fixation of proximal humerus fractures, osteosynthesis of proximal femoral fractures, stabilisation of traumatic and osteoporotic vertebral fractures. In four types of operations (plate osteosynthesis of proximal humeral, distal radial and tibial fractures and intramedullary nailing of the clavicle), anaesthetists` amount of radiation exceeded one-third of the surgeons' exposure, especially if the C-arm tube was positioned close to the anaesthetists work station at the patients' head. Conclusion: By using the occupational radiation dose monitoring system, radiation exposure to anaesthetists was visualised in real-time during trauma operations. Radiation exposure of anaesthetists depends on the type of operation and the position of the C-arm. The system may help to increase anaesthetists` awareness concerning radiation exposure and to enhance compliance in using radiation protection techniques.

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

Background: Leukemia is the most common pediatric malignancy. Central Nervous System (CNS) is the most frequently involved extramedullary location at diagnosis and at relapse. Objective: To determine if Magnetic Resonance Imaging (MRI) findings of optic nerves should contribute to early detection of CNS relapse in pediatric leukemia. Methods: Twenty patients (10 boys, 10 girls; mean age 8,3 years, range 4-16 years) with proven CNS relapse of leukemia followed up between 2009 and 2017 in our institution were included. Orbital MRI exams performed before and during CNS relapse were reviewed retrospectively. Forty optic nerves with Optic Nerve Sheaths (ONS) and Optic Nerve Heads (ONH) were evaluated on fat-suppressed T2-weighted TSE axial MR images. ONS diameter was measured from the point 10 mm posterior to the globe. ONS distension and ONH configuration were graded as 0, 1 and 2. Results: Before CNS relapse, right mean ONS diameter was 4.52 mm and left was 4.61 mm which were 5.68 mm and 5.66 mm respectively during CNS relapse showing a mean increase of 25% on right and 22% on left. During CNS relapse, ONS showed grade 0 distension in 15%, grade 1 in 60%, grade 2 in 25% and ONH demonstrated grade 0 configuration in 70%, grade 1 in 25% and grade 2 in 5% of the patients. Conclusion: MRI findings of optic nerves may contribute to diagnose CNS relapse by demonstrating elevated intracranial pressure in children with leukemia.