Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

International Journal of Computer and Applications

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.

Ultrasound in regional anesthesia (RA) has increased in popularity over the last years. The nerve localization presents a key step for RA practice, it is therefore valuable to develop a tool able to facilitate this practice. The nerve detection in the ultrasound images is a challenging task, since the noise and other artifacts corrupt the visual properties of such kind of tissue. In this paper we propose a new method to address this problem. The proposed technique operates in two steps. As the median nerve belongs to a hyperechoic region, the first step consists in the segmentation of this type of region using the k-means algorithm. The second step is more critical; it deals with nerve structure detection in noisy data. For that purpose, a new descriptor is developed. It combines tow methods median binary pattern (MBP) and Gabor filter to obtain the median Gabor binary pattern (MGBP). The method was tested on 173 ultrasound images of the median nerve obtained from three patients. The results showed that the proposed approach achieves better accuracy than the original MBP, Gabor descriptor and other popular descriptors.

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Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

In this paper, the authors have made an attempt to classify the placenta based on the intensity level of histogram of the ultrasound images of placenta. The medical images are usually low in resolution. Specialized tools are required to assist the medical experts in medical image diagnosis and for further treatment. The image histogram is used to classify the ultrasound images of placenta into normal and abnormal placenta using k nearest neighbor classifier. It is further used to analyze the complications of gestational diabetes mellitus on the growth of the placenta.

/pdf/histogram-based-classification-of-ultrasound-images-of-4mkr8hejbx.pdf

Histogram based Classification of Ultrasound Images of Placenta

medical domain, one of the major challenges faced by the medical experts is the extraction of critical information for medical diagnosis. Specialized tools are necessary to assist the experts in diagnosing the diseases. Information retrieval is difficult in the case of ultrasound medical images due to its low resolution making diagnosis difficult. Gestational diabetes is a form of diabetes, which affects pregnant women. It is believed that the hormones produced during pregnancy reduce a woman's receptivity to insulin, leading to high blood sugar levels. The duration of departures from normogycemia in maternal diabetes is the critical factor. The earlier detection of GDM occurs, the lesser the influence on placental development, which indirectly accounts for fetal growth and metabolism. This pilot study involves the feasibility for classifying the ultrasound images of placenta with complicating diabetes based on placenta thickness using statistical textural features

/pdf/thickness-based-characterization-of-ultrasound-placenta-43mni14r1k.pdf

Thickness Based Characterization of Ultrasound Placenta Images Using Regression Analysis

© 2012 Malathi and Shanthi, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wavelet Image Fusion Approach for Classification of Ultrasound Placenta Complicated by Gestational Diabetes Mellitus

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Wavelet Image Fusion Approach for Classification of Ultrasound Placenta Complicated by Gestational Diabetes Mellitus

With the advancement of internet technology, there is a large volume of information exists on the web for users of the internet. These users not just utilize the accessible resources on the web yet, in addition, give their feedback, consequently creating more useful data. Opinion mining (OM) is contemplated as a subfield of NLP, information retrieval, and data mining. It is the way toward extricating people's thoughts and observations from unformed content, which with respect to the development of online social media and the huge amount of user feedback, has gotten an effective, attractive and furthermore complex issue. The fundamental in OM is classifying the polarity of text as far as positive (good), neutral (surprise) or negative (bad). This paper is proposed to review the methods and applications of opinion mining. This paper presents a review of OM and related strategies and additionally discusses the applications and difficulties for OM with knowledge into previous researchers.

Opinion Mining using Machine Learning Approaches: A Critical Study

Obstructive sleep apnea is a common problem arising in adults and children nowadays, determined by abnormalities in breathing gaps or incapability of air intake capacity during sleeping results in a decrease in oxygen level in blood. The brain detects this sudden decrease in the level of oxygen and sends a signal to wake the person up. Studies revealed the breathing stops for almost 10 seconds during a sleep apnea episode. There is no restriction on who can develop Obstructive Sleep Apnea(OSA), it can affect adults as well as infants. Our research primarily aims at assessing the various recent developments and studies made as a solution to this alarming problem. Their methodology and techniques have been studied and accuracy and sensitivity rates compared. A comprehensive and detailed study has been conducted on several research papers and studies done in the field of predicting sleep apnea. Sleep Apnea and classification of apneic signals have been mentioned in our study. The related researches have been studied extensively and compiled in our research work. The various techniques used by the researchers have been studied and tabulated along with the algorithm accuracies. It is observed that signal measurement along with AI algorithms has made significant advancements in OSA prediction. It is observed that Self Developed Algorithm on VAD showed the highest accuracy of 97%. PPG signal analysis and binary classification algorithm showed good accuracies of 86.67% and 86% respectively. AdaBoost, Decision Table and Bagging REPTree and SVM classifier also showed good accuracy of around 83% in the detection of Sleep Apnea episodes. The study highlighted the research works done to combat the rising problem of Obstructive Sleep Apnea. This comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.

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G. Malathi

Papers

Histogram based Classification of Ultrasound Images of Placenta

Thickness Based Characterization of Ultrasound Placenta Images Using Regression Analysis

Wavelet Image Fusion Approach for Classification of Ultrasound Placenta Complicated by Gestational Diabetes Mellitus

Opinion Mining using Machine Learning Approaches: A Critical Study

A Comprehensive Analysis on the Efficient Mechanisms to Detect Obstructive Sleep Apnea Using AI and Heuristic Algorithms