Intelligent Segmentation of Medical Images using Fuzzy Bitplane Thresholding

The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images

Age-related changes in rat bone-marrow mesenchymal stem cell plasticity

<p>Abstract</p> <p>Background</p> <p>The efficacy of adult stem cells is known to be compromised as a function of age. This therefore raises questions about the effectiveness of autologous cell therapy in elderly patients.</p> <p>Results</p> <p>We demonstrated that the expression profile of stemness markers was altered in BM-MSCs derived from old rats. BM-MSCs from young rats (4 months) expressed Oct-4, Sox-2 and NANOG, but we failed to detect Sox-2 and NANOG in BM-MSCs from older animals (15 months). Chondrogenic, osteogenic and adipogenic potential is compromised in old BM-MSCs. Stimulation with a cocktail mixture of bone morphogenetic protein (BMP-2), fibroblast growth factor (FGF-2) and insulin-like growth factor (IGF-1) induced cardiomyogenesis in young BM-MSCs but not old BM-MSCs. Significant differences in the expression of gap junction protein connexin-43 were observed between young and old BM-MSCs. Young and old BM-MSCs fused with neonatal ventricular cardiomyocytes in co-culture and expressed key cardiac transcription factors and structural proteins. Cells from old animals expressed significantly lower levels of VEGF, IGF, EGF, and G-CSF. Significantly higher levels of DNA double strand break marker γ-H2AX and diminished levels of telomerase activity were observed in old BM-MSCs.</p> <p>Conclusion</p> <p>The results suggest age related differences in the differentiation capacity of BM-MSCs. These changes may affect the efficacy of BM-MSCs for use in stem cell therapy.</p

A Review on Emerging Threats and Vulnerabilities in Internet of Things and its Applications

Nowadays, Internet of Things (IoT) plays a crucial part in the area of Information Technology (IT). At present, providing security to information has become one of the difficult tasks. Recently, the IoT and the devices connected on it have accepted a sizeable attention towards the research. The IoT is contemplated as the future of the Internet. IoT and its connected devices will have a considerable role and will change the style of living, standard of living, as well as the models of business in future. The applications of IoT in various fields are anticipated to increase gradually in the up-coming years. Various types of threats such as malicious based attack, network based attack and network abuse have been emerged and identified in the IoT based on virus, Phishing, Spam and the user abuse. It has been noted that these mechanisms are causing various level of complication and preferment as there is advances in IoT based devices and its technology. This paper focuses on various challenges, threats and vulnerabilities faced by cyber security especially in the field of IoT and its latest technologies. It also focuses on the techniques of security, methods and the recent trends which are changing the face of IoT based security. This paper also focuses on an attempt to classify various types of threats, besides analyzing and characterizing the intruders and attacks facing towards the IoT devices and its services

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively

Smart IoMT-based segmentation of coronavirus infections using lung CT scans

Computed Tomography (CT) is one of the biomedical imaging modalities which are used to confirm COVID-19 cases and/or to identify infected areas in the lung. Therefore, this article aims at assisting this crucial radiological task by proposing squeeze-and-excitation networks (SENets) within the Internet of medical things (IoMT) framework for automated segmentation of COVID-19 infections in lung CT images. The proposed SE block has been directly integrated with deep residual networks to form Seresnets based on U-Net and LinkNet models. Extensive tests were conducted on a public COVID-19 CT dataset including 20 cases and 1800 + annotated slices to evaluate the segmentation results of our proposed method. The proposed Seresnet models showed a good performance with a Dice score of 0.73, structure similarity index of 0.98, enhanced alignment measure of 0.98, and mean absolute error of 0.06. This study demonstrated a new advanced tool for radiologists to achieve automatic segmentation of the COVID-19 infected areas using CT scans. The main prospect of this research work is deploying our proposed IoMT segmentation framework in the medical diagnosis routine of positive COVID-19 patients