Titlebook: Biomedical Image Analysis; Special Applications Pritpal Singh Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive li

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Introduction,To develop pattern recognition and vision method for medical images has become one of the most challenging tasks in view of practical as well as industrial interest.

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https://doi.org/10.1007/978-981-99-9939-2Fuzzy set; Neutrosophic set; Clustering; Segmentation; MRIs; CT scans

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https://doi.org/10.1007/978-3-642-96430-5 Radiologists and medical practitioners mostly depended on the analysis of PD patients’ magnetic resonance images (MRIs) to identify this disease. Due to presence of grayscale features and uncertain inherited information in MRIs, their pattern recognition and visualization were very complex. With th

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https://doi.org/10.1007/978-3-642-96430-5ecisions through evaluating the developments in these regions. Study of these MRIs suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures.

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https://doi.org/10.1007/978-981-10-0173-4ases by evaluating the developments in these areas. One of the significant approaches used in analyzing the MRIs were segmenting the regions. However, their segmentation suffers from two major problems as: (a) the boundaries of their gray matter and white matter regions are ambiguous in nature, and

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https://doi.org/10.1007/978-981-10-0173-4s and helps to model uncertainties with six different memberships very effectively. To demonstrate the real-time application of this theory, a new segmentation method for brain tumor tissue structures in magnetic resonance imaging (MRI) is presented. There are inconsistencies in the gray levels obse