Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/582305
Title: Medical Image Segmentation Using Multithreshold Based Approaches
Researcher: DHARMENDRA KUMAR
Guide(s): Anil Kumar Solanki and Anil Kumar Ahlawat
Keywords: Computer Science
Computer Science Information Systems
Engineering and Technology
University: Dr. A.P.J. Abdul Kalam Technical University
Completed Date: 2024
Abstract: The field of medical imaging has witnessed unprecedented advancements in recent newlineyears, offering a wealth of diagnostic tools crucial for healthcare professionals in newlinedisease detection, treatment planning, and monitoring patient progress. Among these newlinetechnologies, medical image segmentation stands as a fundamental process, serving as a newlinecornerstone for accurate analysis and extraction of meaningful information from complex newlineimages. newlineThis thesis endeavors to delve into the realm of medical image segmentation, focusing newlineon the utilization of multi-threshold-based approaches to enhance the precision and newlineefficacy of this critical process. Specifically, our research concentrates on proposing newlineinnovative methodologies for pre-processing and segmentation techniques tailored for newlinedigital mammograms. newlineThe initial phase of our study centers on refining the preprocessing phase through newlineluminosity control and contrast enhancement techniques applied to digital mammograms. newlineThese enhancements aim to optimize the visual quality of images, thus improving newlinesubsequent segmentation outcomes. newlineFurthermore, our proposed method for segmentation involves a modified Fuzzy C newlineMean based clustering algorithm. This innovative approach harnesses the power of newlinemodified fuzzy clustering techniques to accurately delineate regions of interest within newlinemedical images, particularly in the domain of mammography. newlineAdditionally, our research extends to the development and exploration of multilevel newlinethresholding-based medical image segmentation. Here, the researcher proposed a novel newlinehybrid optimization approach for medical image segmentation utilizing nature-inspired newlinealgorithms (NIAs). This strategy aims to exploit the efficiency and adaptability found newlinein nature to achieve enhanced segmentation results, particularly in complex and intricate newlinemedical images. newlineThe research work presented in this thesis aims to significantly contribute to the newlinefield of medical image segmentation by introducing and refining multi-threshold-based newlineapproaches. The integration of pre-processing t
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URI: http://hdl.handle.net/10603/582305
Appears in Departments:dean PG Studies and Research

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80_recommendation.pdfAttached File440.73 kBAdobe PDFView/Open
annexures.pdf97.05 kBAdobe PDFView/Open
chapter 1.pdf71.93 kBAdobe PDFView/Open
chapter 2.pdf126.75 kBAdobe PDFView/Open
chapter 3.pdf1.12 MBAdobe PDFView/Open
chapter 4.pdf387.34 kBAdobe PDFView/Open
chapter 5.pdf467.69 kBAdobe PDFView/Open
chapter 6.pdf362.93 kBAdobe PDFView/Open
chapter 7.pdf52.01 kBAdobe PDFView/Open
prelim pages.pdf427.91 kBAdobe PDFView/Open
title.pdf228.25 kBAdobe PDFView/Open
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