Biomedical Signal and Image Examination with Entropy-Based Techniques

This book discusses entropy-based image pre-processing methods for analyzing biomedical signals/images,with applications in x-ray,MRI,and CT analysis. It is intended for medical signal/image analysts,students,researchers,and medical scientists.

Format: Paperback / softback
Length: 122 pages
Publication date: 25 September 2023
Publisher: Taylor & Francis Ltd

The aim of this comprehensive book is to provide a detailed and comprehensive exploration of the concept of entropy, encompassing its various types and their practical applications in evaluating biomedical signals and images. It emphasizes the significance of entropy-based image pre-processing techniques in developing efficient computerized examination systems for analyzing biomedical images captured using diverse modalities. The book delves into the application of entropy measures, including Kapur, Tsallis, Shannon, and Fuzzy, on a diverse range of medical images, such as RGB-scaled and gray-scaled images. Through rigorous case studies involving x-ray image analysis, MRI analysis, and CT analysis, the effectiveness of the proposed techniques is substantiated. This book is designed to serve as a valuable resource for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists with an interest in biomedical data evaluation.

Entropy is a fundamental concept in thermodynamics that quantifies the disorder or randomness of a system. In the context of biomedical signals and images, entropy plays a crucial role in understanding the complexity and information content of these data. By analyzing the entropy of biomedical signals and images, researchers can gain insights into the physiological condition of the patient, as well as the nature and severity of any diseases.

One of the most important applications of entropy-based image pre-processing techniques is in the development of computerized examination systems for analyzing biomedical images. These systems are used in a wide range of medical applications, including radiology, pathology, and dermatology. By applying entropy-based image pre-processing techniques, researchers can enhance the visibility, contrast, and clarity of biomedical images, making them more useful for diagnosis and treatment.

There are several entropy-based image pre-processing methods that have been developed in recent years. These methods include entropy-based thresholding, entropy-based contrast enhancement, and entropy-based feature extraction. Entropy-based thresholding is a technique that involves setting a threshold value to determine the intensity of pixels in an image. By setting the threshold value based on the entropy of the image, researchers can enhance the visibility of subtle features and reduce the noise in the image. Entropy-based contrast enhancement is a technique that involves enhancing the contrast of an image by adjusting the intensity of pixels based on their entropy values. This technique can help to highlight the boundaries between different tissues and structures in an image, making it easier for doctors to diagnose diseases. Entropy-based feature extraction is a technique that involves extracting features from an image based on its entropy values. These features can include texture, shape, and intensity, which can be used to classify and diagnose diseases.

In addition to these entropy-based image pre-processing methods, there are also other techniques that have been developed for analyzing biomedical signals and images. These techniques include wavelet transform, fractal analysis, and machine learning. Wavelet transform is a technique that involves decomposing an image into a set of subbands, each of which is represented by a different frequency. By analyzing the frequency content of the subbands, researchers can gain insights into the texture and shape of the image. Fractal analysis is a technique that involves analyzing the fractal properties of an image. Fractals are geometric shapes that exhibit self-similarity, meaning that they can be divided into smaller copies of themselves that still retain the same shape. By analyzing the fractal properties of an image, researchers can gain insights into the complexity and irregularity of the image. Machine learning is a technique that involves training a machine to learn from data. By analyzing the data, the machine can learn to recognize patterns and make predictions. Machine learning has been used in a wide range of medical applications, including cancer diagnosis, heart disease diagnosis, and stroke diagnosis.

In conclusion, entropy-based image pre-processing techniques are essential for the development of efficient computerized examination systems for analyzing biomedical signals and images. These techniques can enhance the visibility, contrast, and clarity of biomedical images, making them more useful for diagnosis and treatment. In addition to these entropy-based image pre-processing methods, there are also other techniques that have been developed for analyzing biomedical signals and images, such as wavelet transform, fractal analysis, and machine learning. These techniques can provide researchers with additional insights into the complexity and information content of biomedical signals and images.

Weight: 453g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367686840