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

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Sparse representation-based MRI super-resolution reconstruction

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

Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.

Statistical Measurement of Ultrasound Placenta Images Complicated by Gestational Diabetes Mellitus Using Segmentation Approach.

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 Diagnosis is the utmost need of an hour. Gestational Diabetics in women represents the second leading cause of yielding children born with birth defects. The ultrasound images are usually low in resolution making diagnosis difficult. Specialized tools are required to assist the medical experts to categorize and diagnose diseases to accuracy. If the anomalies in the ultrasound images are detected in the preliminary screening of placenta, fetal loss could be minimized. This pilot study was carried out to find the feasibility for detecting anomalies in placental growth due to the implications of gestational diabetics by considering the stereo image mapping based on wavelet analysis for 2D reconstruction. The research uses wavelet based methods to extract features from the ultrasonic images of placenta. The shape of the placenta is generated using the Back Propagation Network. Euclidean Distance Classifier is used for classifying the ultrasonic images of placenta.

Wavelet Based Features for Ultrasound Placenta Images Classification

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

/pdf/wavelet-image-fusion-approach-for-classification-of-4zlmapztly.pdf

V. Shanthi

Papers

Statistical Measurement of Ultrasound Placenta Images Complicated by Gestational Diabetes Mellitus Using Segmentation Approach.

Histogram based Classification of Ultrasound Images of Placenta

Wavelet Based Features for Ultrasound Placenta Images Classification

Thickness Based Characterization of Ultrasound Placenta Images Using Regression Analysis

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