Analysis and Comparison of Developed 2D Medical Image Database Design using Registration Scheme, Retrieval Scheme, and Bag-of-Visual-Words

There is certain to be a significant increase in the use of registration, retrieval and registration based retrieval of medical images in healthcare. But there is a related major concern that these initiatives are narrowly policy or technology led rather than evidence based or case based reasoning. Or else, either they will be based on old paradigm structures without considering the new paradigms that they create. It is planned to look at the image registration and retrieval from a unified perspective based on their performance for clinical diagnosis and treatment. It is concluded with an experiment carried out on histopathology and five types of anatomic (radiology) images. The results showed that this approach works better with more images from the Bag-Of-Visual words and Affine with B-Spline registration based retrieval for modality and histopathological images. Bag-of-Visual Words is more suitable for histopathological images than registration based retrieval techniques.

Wireless Body Area Networks Combined with Mobile Cloud Computing in Healthcare

This chapter introduces the combination of wireless body area network and mobile cloud computing in healthcare. The increased growth of low-power integrated circuits, physiological sensors and wireless communication has introduced a new generation of wireless sensor networks. Cloud computing is on high demand, whereas in case of mobile cloud computing the device is much more user friendly to manage the information. The combination of wireless body area network (WBAN) and mobile cloud computing (MCC) promises a better performance to the users immediately. It is more feasible to wire a sensor which performs the required medical tests and provides the information through devices like mobile phones and tablets. In this chapter, a theoretical study on the combination of WBAN and mobile cloud computing has been done.

Classification of Surface Electromyogram Signals Acquired from the Forearm of a Healthy Volunteer

Surface EMG (sEMG) signals from the palmaris longus, flexor carpi radialis and flexor carpi ulnaris muscles were recorded using an in-house developed EMG signal acquisition system. The bandwidth of the acquisition system was 1500 Hz. The extracted sEMG signal was processed using Discrete Wavelet Transform (DWT). The features of the extracted and the wavelet processed signals were determined and were used for probable classification using Artificial Neural Network (ANN). A classification efficiency of more than 90% was achieved using ANN classifiers. The results suggested that the sEMG may be successfully used for designing efficient control system.

New Imaging and Computational Technology as a Guide for Catheter Ablation of Incessant Tachyarrhythmias

Computational technology in the era of catheter ablation (RFA) has made it possible to experience relief from incessant atrial tachyarrhythmias (AT) by 3D electroanatomical mapping (EAM) systems. The Authors report the results of such technology in > 500 consecutive patients (57% males, mean age 56.9 years) with incessant refractory post-ablation left AT (mean cycle length 256 ms). Patients underwent electroanatomical-mapping systems, which combine electrophysiological and spatial information allowing accurate reconstruction of the whole atria with real-time activation sequence guiding RFA for continuous transmural linear lesions. Color-coded voltage and/or activation maps were successfully performed in all patients. Mapping distinguished clearly and rapidly between micro-macro-reentrant (>80%) and focal mechanisms. Acute success was obtained without major complications, with repeated procedures in about 5% of patients. EAM technology allows determining both mechanism and location of arrhythmia, ensuring successful elimination of complex arrhythmogenic substrates.

Effect of Slow and Fast Music on the Autonomic Nervous System and Cardiac Health

In this study, the effect of slow and fast music on the heart rate variability and conduction pathway of the heart was studied. The results indicated an increase in the parasympathetic dominance as the volunteers were made to listen to music. The magnitude of the parasympathetic activity was higher when the volunteers were made to listen to fast music. This indicates that slow and fast music affected the sympatho-vagal balance in different proportions. The analysis of the ECG signal and wavelet transformed ECG signal suggested an alteration in the conduction pathway of the heart.

Classification of Magnetic Resonance Image and Segmentation of Brain Tissues for Tumor Detection

Advancing techniques in image processing has led to many inventions and provides valuable support especially in medical fields to identify and analyze the diseases. MRI images are chosen for detection of brain tumor as they are used in soft tissue determinations. Brain tumor is one of the severe diseases in the field of medicine. Early identification of disease increases the chances for successful treatment. Classification and Segmentation plays a vital role in identifying the disease. First, image Pre-processing is used to enhance the image quality. Subsequently, Decomposition is performed using Dual-Tree Complex Wavelet Transform to analysis texture of an image and features are extracted using Gray-Level Co-Occurrence Matrix. Then, Neuro-Fuzzy and Neural Network can be used to categorize the types of Brain Tumor such as normal, benign and malignant. Finally, tumor region is detected using Kernel weighted clustering method by segmenting the brain tissues and also to find the size of the tumor.

Intensity-Based Classification and Related Methods in Brain MR Images

Today, medical image processing and analysis are highly active research fields boosted by rapid technical developments in medical imaging field. This chapter describes common procedures such as thresholding methods and clustering algorithms (both non-hierarchical and hierarchical approaches) used for digital image processing, with specific reference to brain magnetic resonance images. These techniques represent starting points for other sophisticated methods such as segmentation and classification. The results, which are an outcome of these methods, are used for classification of neurodegenerative diseases such as Alzheimer, Pick's, Huntington's or cerebral calcinosis. A number of applications together with the code listing are provided with the aim to make the subject accessible and practical. The MATLAB software will help the readers to identify and choose the best solution for a particular problem.

The Combination of Adaptive Filters to Improve the Quality of Medical Images in New Wavelet Domain

Most of medical images not only have noise but also have blur. This problem reduces the quality of images and influences diagnostic process of medical specialists because a small detail in a medical image is very useful for treatment process. This chapter explores the new generation wavelets, which provides the basic framework for the development of adaptive techniques to improve the quality of medical images. The process of the method for improving medical images includes: decompose of medical images in nonsubsampled contourlet domain and calculate the coefficients of Bayesian thresholding combined with Lucy Richard to reconstruct the medical images. For demonstrating the superiority of the method, the results of the proposed method are compared with the results of the other methods in new generation wavelet domain.

Bag-of-Features in Microscopic Images Classification

Microscopic image analysis plays a foremost role for understanding biological processes, diagnosis of diseases and cells/ tissues identification. Microscopic image classification is one of the challenging tasks that have a leading role in the medical domain. In this chapter, an overview on different classification techniques elaborated with microscopic images is presented to guide the reader through the advanced knowledge of major quantitative image classification approaches. Applied examples are conducted to classify different Albino rats' samples captured using light microscope for three different organs, namely hippocampus, renal and pancreas. The Bag-of-Features (BoF) technique was employed for features extraction and selection. The BoF selected features were used as input to the multiclass linear support vector machine classifier. The proposed classifier achieved 94.33% average classification accuracy for the three classes. Additionally, for binary classification the achieved average accuracy was 100% for hippocampus and pancreas sets classification.

Development of a Surface EMG-Based Control System for Controlling Assistive Devices

The current study discusses about the development of an EMG based wireless control system for the patients suffering from high-level motor disability. Surface EMG (sEMG) signals were processed in the time domain and using discrete wavelet transforms (DWT). The statistical features of the signals (sEMG, envelope of the squared sEMG and wavelet processed sEMG) were determined and analyzed. The analysis of the features suggested that the features of the envelope of the squared sEMG signals were sufficient to be used for high-efficiency classification and control signal generation. A hall-effect sensor based switching mechanism was introduced for controlling the duration of the activation of the device. The control signals were wirelessly transmitted to the assistive device (robotic vehicle). The training and the subsequent use of the developed control system were easy.