Despeckling Algorithms for Optical Coherence Tomography Images

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology. The presence of speckle affects the quality of OCT images. Despeckling is necessary to improve its visual quality, and it is an integral part of software packages used for the computerized analysis of OCT. Even though a few methods for despeckling OCT are available in the literature, a cross-comparison of their performance is not known to be available. In this chapter, the techniques available in the literature for despeckling the OCT images have been identified. The results of the despeckling algorithms are compared both qualitatively and quantitatively by concerning the noise suppression capability and feature preservation. Among the available techniques, iterative adaptive unbiased (IAUB) filter is found to be superior as far as its performance regarding despeckling on retinal OCT images.

Classification Algorithms for EEG-Based Brain-Computer Interface

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

Melanoma Image Classification Based on Multivariate Parametric Statistical Tests of Hypothesis

This chapter proposes a novel method, based on the multivariate parametric statistical tests of hypotheses, which classifies the normal skin lesion images and the various stages of the melanoma images. The melanoma images are categorized into two classes, such as initial stage and advanced stage, based on the degree of aggressiveness of the cancer. The region of interest is identified and segmented from the input skin melanoma image. The features, such as HSV color, shape, and texture, are extracted from the region of interest. The features are treated as a feature space, which is assumed to be a multivariate normal random field. The proposed statistical tests are employed to identify and classify the melanoma images. The proposed method yields an average correct classification up to 91.55% for the normal skin lesion versus the initial and the advanced stages of the melanoma images, up to 91.39% for initial stage melanoma versus the normal skin lesion and the advanced stages melanoma, and up to 92.27% for the advanced stage melanoma versus the normal skin lesion and the initial stage melanoma. The proposed method yields better results.

Developing an Effective Classification Model for Medical Data Analysis

The sparse nature of medical data makes knowledge discovery and prediction a complex task for analysis. Machine learning algorithms have produced promising results for diversified data. This chapter constructs the effective classification model for medical data analysis. In particular, nine classification models, namely Naïve Bayes, decision tree (i.e., J48 and Random Forest), multilayer perceptron, radial bias function, k-nearest neighbors, single conjunctive rule learner, support vector machine, and simple logistics have been applied for developing an effective model. Besides, classification models have also been used in conjunction with ensemble learning methods, since ensemble methods significantly increase the predictive outcomes of the classification models. The evaluation of classification models has been measured using accuracy, f-measure, precision, and recall metrics. The empirical results revealed that the combination of ensemble learning methods with classification models produces better predictions in comparison with sole classification model for the medical data.

Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, f-measure, and AUC) are used and for implementation. The authors have used python language. The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction.

Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis

In this chapter, different types of image fusion techniques have been studied and evaluated in the medical applications. The ultimate goal of this proposed method is to obtain the fused image without any loss of similar information and preserve all special features present in the input medical images. This method is used to improve the fused image quality for better diagnosis of critical disease analysis. The fused hybrid multimodal medical image should convey better visual description than the individual input images. This chapter proposes the method for multimodal medical image fusion using the hybrid fusion algorithm. The computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input images used for this experimental work. In this chapter, experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms.

Medical Image Segmentation and Analysis

An improved energy-based technique with a Lattice Boltzmann method organizes with the neighborhood and global energy terms, local term propels to pull the frame and constrain it to protest limit, decides noteworthy points of interest not confined to, snappy planning, automation, invariance of exact medical image segmentation, and analysis. Consequently, the worldwide vitality fitting term drives the advancement of the frame at a division of the question limit. The worldwide vitality term relies upon the worldwide division computation, which can better catch drive information of pictures than mixture area-based dynamic shape technique. Both neighborhood and worldwide terms are ordinarily acclimatized to construct a level set strategy to divide pictures with exactness. The level set technique with Boltzmann system uses neighborhood mean, a quality which engages it as far as possible. The proposed chapter gathers gainful purposes of intrigue not stuck just using expedient process, computerization, and right helpful picture partitions.

Prevalence of Musculoskeletal Disorders of Odisha Farmers in Selected Agricultural Tasks

In the chapter, there are dual main contributions. In the first phase, based on the extensive review of literature on the application of cuckoo search (CS) methodology, its application for the optimization of agricultural pesticide sprayers for maximum efficiency was suggested. In the second phase of study, 75 farmers of Odisha in India were considered to assess their musculoskeletal disorders (MSDs) during seeding, fertilizing, and weeding of crops using a Standardized Nordic Questionnaire with a five point rating scale (i.e., 1 = Very less, 2 =Less, 3 = Nil, 4 = Strong, 5 = Very Strong). Factor analysis was performed for “seeding, fertilizing, and weeding characteristics,” “economical characteristics,” and “tools and equipment characteristics of farmers.” Then Pearson correlation coefficient matrix was generated for the seeding, fertilizing, and weeding characteristics of farmers, followed by regression analysis for the economic characteristics of farmers.

Advances in Ultrasound Despeckling

The B-mode ultrasound images are corrupted due to the presence of speckle noise. Hence, the speckle removal in the ultrasound images is essential for proper clinical examination and quantitative assessments. The speckle pattern varies with several imaging parameters as well as the anatomical structure in the image. It is hard to avoid speckle by performing averaging and low noise system designs. An excessive speckle reduction diminishes the visibility of small anatomical structures and thereby makes the image understanding complicated. This chapter is intended to encapsulate various techniques for reducing speckle in medical ultrasound images and improving the image quality for visual inspection and/or computer-assisted diagnosis of ultrasound images. In addition, the chapter surveys the papers published between 2015 and 2018 to highlight the latest trends in the despeckling of ultrasound images. The chapter also presents the performance comparison of a few popular algorithms to despeckle medical ultrasound images.

Heart Disease Diagnosis

In the modern era of information technology, machine learning algorithms are used in different domains for boosting the quality of decision making. The correct decision making about the disease diagnosis is one of the applications where these approaches are applied successfully for assisting the doctors. Correct and timely diagnosis of disease is the primary requirement of effective treatment. Today, one of the most leading causes of death is heart disease. This chapter deals with the application of different machine learning algorithms for effective heart disease diagnosis. Diagnosis through the machine learning algorithms involves the three major steps, data preprocessing, feature selection, and classification. The chapter covers the experimental study of performance of SVM, ANN, logistic regression, random forest, KNN, AdaBoost, Naive Bayes, decision tree, SGD, CN2 rule inducer approaches.