Current Medical Imaging - Volume 18, Issue 5, 2022

Background: Cancer is one of the life-threatening diseases which is affecting a large number of population worldwide. Cancer cells multiply inside the body without showing much symptoms on the surface of the skin, thereby making it difficult to predict and detect the onset of the disease. Many organizations are working towards automating the process of cancer detection with minimal false detection rates. Introduction: The machine learning algorithms serve to be a promising alternative to support health care practitioners to rule out the disease and predict the growth with various imaging and statistical analysis tools. Medical practitioners are utilizing the output of these algorithms to diagnose and design the course of treatment. These algorithms are capable of finding out the risk level of the patient and can reduce the mortality rate concerning cancer disease. Method: This article presents the existing state of art techniques for identifying cancer affecting human organs based on machine learning models. The supported set of imaging operations is also elaborated for each type of cancer. Conclusion: The CAD tools are the aid for the diagnostic radiologists for preliminary investigations and detecting the nature of tumor cells.

A defected ground antenna with dielectric reflector is designed and investigated for breast tumour diagnosis. Ultra-wide band resonance (3.1 to 10.6 GHz) is achieved by etching two slots and adding a narrow vertical strip in a patch antenna. A high dielectric constant substrate is added below the antenna, which shows remarkable effect on performance. Antenna performance is verified experimentally on an artificially fabricated breast tissue and tumour. Malignant tissue has different dielectric properties than the normal tissue which causes deviation in the scattered antenna power. Average value of backscattered signal variation and ground penetrating radar (GPR) algorithm is used to localize the tumour of radius 4mm in breast tissue.

Background: Obtaining the medical history from a patient is a tedious task for doctors as it depends on a lot of factors which are difficult to keep track from a patient’s perspective. Doctors have to rely upon technological tools to make a swift and accurate judgment about the patient’s health. Introduction: Out of many such tools, there are two special imaging modalities known as X-ray - Computed Tomography (CT) and Magnetic Resonance imaging (MRI) which are of significant importance in the medical world assisting the diagnosis process. Methods: The advancement in signal processing theory and analysis has led to the design and implementation of a large number of image processing and fusion algorithms. Each of these methods has evolved in the terms of their computational efficiency and visual results over the years. Results: Various researches have revealed their properties in terms of their efficiency and outreach and it has been concluded that image fusion can be a very suitable process that can help to compensate for the drawbacks. Conclusion: In this manuscript, recent state-of-the-art techniques have been used to fuse these image modalities and established its need and importance in a more intuitive way with the help of a wide range of assessment parameters.

Background: The new coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Artificial Intelligence (AI) assisted identification and detection of diseases is an effective method of medical diagnosis. Objectives: To present recent advances in AI-assisted diagnosis of COVID-19, we introduce major aspects of AI in the process of diagnosing COVID-19. Methods: In this paper, we firstly cover the latest collection and processing methods of datasets of COVID-19. The processing methods mainly include building public datasets, transfer learning, unsupervised learning and weakly supervised learning, semi-supervised learning methods and so on. Secondly, we introduce the algorithm application and evaluation metrics of AI in medical imaging segmentation and automatic screening. Then, we introduce the quantification and severity assessment of infection in COVID-19 patients based on image segmentation and automatic screening. Finally, we analyze and point out the current AI-assisted diagnosis of COVID-19 problems, which may provide useful clues for future work. Conclusion: AI is critical for COVID-19 diagnosis. Combining chest imaging with AI can not only save time and effort, but also provide more accurate and efficient medical diagnosis results.

Background: Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time. Introduction: During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling. Methods: This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing. Results: Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue. Conclusion: Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert’s burden.

Background: Hemoglobin is an essential biomolecule for the transportation of oxygen, therefore, its assessment is also important to be done frequently in numerous clinical practices. Traditional invasive techniques have concomitant shortcomings, such as time delay, the onset of infections, and discomfort, which necessitate a non-invasive hemoglobin estimation solution to get rid of these constraints in health informatics. Currently, various techniques are underway in the allied domain, and scanty products are also feasible in the market. However, due to the low satisfaction rate, invasive solutions are still assumed as the gold standard. Recently introduced technologies effectively evolved as optical spectroscopy and digital photographic concepts on different sensing spots, e.g., fingertip, palpebral conjunctiva, bulbar conjunctiva, and fingernail. Productive sensors develop more than eight wavelengths to compute hemoglobin concentration and four wavelengths to display only Hb-index (trending of hemoglobin) either in disposable adhesive or reusable cliptype sensor’s configuration. Objective: This study aims at an optimistic optical spectroscopic technique to measure hemoglobin concentration and conditional usability of non-invasive blood parameters’ diagnostics at point-ofcare. Methods: Two distinguishable light emitting sources (810 nm and 1300 nm) are utilized at isosbestic points with a single photodetector (800-1700 nm). With this purpose, reusable finger probe assembly is facilitated in transmittance mode based on the newly offered sliding mechanism to block ambient light. Results: Investigation with proposed design presents correlation coefficients between reference hemoglobin and every individual feature, a multivariate linear regression model for highly correlated independent features. Moreover, principal component analytical model with multivariate linear regression offers mean bias of 0.036 and -0.316 g/dL, precision of 0.878 and 0.838 and limits of agreement from -1.685 to 1.758 g/dL and -1.790 to 1.474 g/dL for 18 and 21 principal components, respectively. Conclusion: The encouraging readouts emphasize favorable precision; therefore, it is proposed that the sensing system is amenable to assess hemoglobin in settings with limited resources and strengthening future routes for the point of care applications.

Objective: The objective of any multimodal medical image fusion algorithm is to assist a radiologist for better decision-making during the diagnosis and therapy by integrating the anatomical (magnetic resonance imaging) and functional (positron emission tomography/ single-photon emission computed tomography) information. Methods: We proposed a new medical image fusion method based on content-based decomposition, Principal Component Analysis (PCA), and sigmoid function. We considered Empirical Wavelet Transform (EWT) for content-based decomposition purposes since it can preserve crucial medical image information such as edges and corners. PCA is used to obtain initial weights corresponding to each detail layer. Results: In our experiments, we found that direct usage of PCA for detail layer fusion introduces severe artifacts into the fused image due to weight scaling issues. In order to tackle this, we considered using the sigmoid function for better weight scaling. We considered 24 pairs of MRI-PET and 24 pairs of MRI-SPECT images for fusion, and the results are measured using four significant quantitative metrics. Conclusion: Finally, we compared our proposed method with other state-of-the-art transformbased fusion approaches, using traditional and recent performance measures. An appreciable improvement is observed in both qualitative and quantitative results compared to other fusion methods.

Objectives: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause Acute Respiratory Distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures. Methods: Nineteen separately colored layers were created from X-ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. In a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected unclear abnormalities varied slightly. Results: The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infections compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing X-rays into five severity levels. Conclusion: By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.

Background: Dealing with the COVID-19 pandemic has been one of the most important objectives of many countries.Intently observing the growth dynamics of the cases is one way to accomplish the solution for the pandemic. Introduction: Infectious diseases are caused by a micro-organism/virus from another person or an animal. It causes difficulty at both the individual and collective levels. The ongoing episode of COVID-19 ailment, brought about by the new coronavirus first detected in Wuhan, China, and its quick spread far and wide revived the consideration of the world towards the impact of such plagues on an individual’s everyday existence. We suggested that a basic structure be developed to work with the progressive examination of the development rate (cases/day) and development speed (cases/day2) of COVID-19 cases. Methods: We attempt to exploit the effectiveness of advanced deep learning algorithms to predict the growth of infectious diseases based on time series data and classification based on symptoms text data and X-ray image data. The goal is to identify the nature of the phenomenon represented by the sequence of observations and forecasting. Results: We concluded that our good habits and healthy lifestyle prevent the risk of COVID-19. We observed that by simply using masks in our daily lives, we could flatten the curve of increasing cases.Limiting human mobility resulted in a significant decrease in the development speed within a few days, a deceleration within two weeks, and a close to fixed development within six weeks. Conclusion: These outcomes authenticate that mass social isolation is a profoundly viable measure against the spread of SARS-CoV-2, as recently recommended. Aside from the research of country- by-country predominance, the proposed structure is useful for city, state, district, and discretionary region information, serving as a resource for screening COVID-19 cases in the area.

Background: Neonatal pneumomediastinum is seen in 2.5 per 1000 live births and is mostly managed conservatively. An intervention is essential in cases with tension pneumomediastinum. Ultrasonography-guided (USG-guided) relief of pneumomediastinum has been reported in newborns. There are no reported cases of computed tomography-guided (CT-guided) drainage of pneumomediastinum in neonates. Case Presentation: A newborn girl born at 34 weeks of gestation was intubated due to respiratory distress and received intratracheal surfactant treatment. Pneumomediastinum was detected at the chest X-ray on the 6th postnatal hour. On the second postnatal day, the patient's oxygen needs increased, tachypnea and subcostal retractions recurred, so it was decided to intervene. USG-guided drainage of the pneumomediastinum was attempted twice but was unsuccessful. Percutaneous drainage with CT guidance was performed successfully. Conclusion: This report aims to emphasize that CT-guided intervention of pneumomediastinum can be an effective alternative in a newborn if USG-guided intervention fails.