Fuzzy Sets Methods in Image Processing and Understanding: Medical Imaging Applications

This book provides a comprehensive overview of recent methods using higher-level information for advanced tasks in medical image understanding, with illustrations from the medical imaging domain. It is an ideal resource for graduate students and researchers.

Format: Hardback
Length: 302 pages
Publication date: 02 January 2023
Publisher: Springer International Publishing AG

This comprehensive book delves into the latest techniques utilizing higher-level information, such as objects or scenes, for advanced tasks like image understanding, with a particular focus on medical images. It presents advanced methods for fuzzy image processing and comprehension, encompassing areas such as fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. Each methodological aspect is accompanied by illustrative examples from the medical imaging domain, making it an invaluable resource for graduate students and researchers in the field of medical image processing.

Introduction:
The field of medical image processing has witnessed significant advancements in recent years, driven by the demand for accurate and efficient diagnosis and treatment. Traditional image processing techniques, which rely on low-level features such as edges and textures, have limitations in capturing the complex characteristics of medical images. As a result, there is a growing need for advanced methods that can leverage higher-level information, such as objects or scenes, to improve image understanding and analysis.

Fuzzy Image Processing:
Fuzzy image processing is a powerful tool for dealing with uncertainty and imprecision in image analysis. It involves the use of fuzzy sets, which are mathematical models that represent the degree of membership or non-membership of an element to a set. Fuzzy image processing techniques can be used to enhance image features, reduce noise, and perform image segmentation.

Fuzzy Spatial Objects:
Fuzzy spatial objects are a fundamental concept in fuzzy image processing. They represent the spatial relationships between objects in an image, taking into account the fuzziness of the boundaries between them. Fuzzy spatial objects can be used for tasks such as object detection, tracking, and image segmentation.

Geometry and Topology:
Geometry and topology play a crucial role in the analysis and understanding of medical images. They provide a mathematical framework for describing the shape, size, and spatial relationships of objects in an image. Geometric and topological techniques can be used for tasks such as shape analysis, registration, and image segmentation.

Mathematical Morphology:
Mathematical morphology is a branch of computer vision that focuses on the analysis and manipulation of shapes. It involves the use of mathematical operators, such as dilation, erosion, and morphological transformations, to analyze and modify the shape of objects in an image. Mathematical morphology techniques can be used for tasks such as object extraction, shape matching, and image segmentation.

Machine Learning:
Machine learning is a rapidly evolving field that has revolutionized the field of image processing. It involves the use of algorithms to learn from data and make predictions or decisions. Machine learning techniques can be used for tasks such as image classification, object detection, and image segmentation.

Verbal Descriptions of Image Content:
Verbal descriptions of image content are a powerful tool for describing the content of medical images. They involve the use of natural language to describe the visual features of an image, such as shapes, colors, and textures. Verbal descriptions can be used for tasks such as image retrieval, annotation, and image understanding.

Fusion:
Fusion is a technique that combines multiple images or modalities into a single image or dataset. It involves the use of algorithms to integrate the information from different sources and create a more comprehensive representation of the object or scene. Fusion techniques can be used for tasks such as multimodal image analysis, tumor detection, and image-guided therapy.

Spatial Relations:
Spatial relations are a fundamental concept in computer vision that describes the relationships between objects in an image. They involve the use of geometric and topological concepts to describe the spatial relationships between objects, such as distance, orientation, and proximity. Spatial relations techniques can be used for tasks such as object tracking, scene understanding, and image registration.

Structural Representations:
Structural representations are a powerful tool for representing the structure and organization of objects in an image. They involve the use of graph-based models to represent the relationships between objects and their spatial relationships. Structural representations can be used for tasks such as object recognition, scene understanding, and image segmentation.

Applications to Medical Images:
Medical images are a rich source of information that can be used for a wide range of applications, including diagnosis, treatment planning, and research. Advanced methods for fuzzy image processing and understanding have been developed specifically for medical images, taking into account the unique characteristics and challenges of this domain.

Conclusion:
In conclusion, this comprehensive book provides a thorough overview of recent methods using higher-level information for advanced tasks such as image understanding, with a particular focus on medical images. It presents advanced methods for fuzzy image processing and comprehension, encompassing areas such as fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. Each methodological aspect is accompanied by illustrative examples from the medical imaging domain, making it an invaluable resource for graduate students and researchers in the field of medical image processing.

Weight: 641g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031194245
Edition number: 1st ed. 2023