Enhancement of discrete wavelet transform algorithm applied in medical image compression (Record no. 37402)

000 -LEADER
fixed length control field 02233nam a22001817a 4500
003 - CONTROL NUMBER IDENTIFIER
control field FT8922
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260107132238.0
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9 A43 C37 2025
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Castro, Rona Jean B.; Sanchez, Niño Angelo A.
245 ## - TITLE STATEMENT
Title Enhancement of discrete wavelet transform algorithm applied in medical image compression
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice c2025
300 ## - PHYSICAL DESCRIPTION
Other physical details Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336 ## - CONTENT TYPE
Source text
Content type term text
Content type code text
337 ## - MEDIA TYPE
Source unmediated
Media type term unmediated
Media type code unmediated
338 ## - CARRIER TYPE
Source volume
Carrier type term volume
Carrier type code volume
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note ABSTRACT: The Discrete Wavelet Transform (DWT) is a widely used technique in medical image compression due to its ability to capture both spatial and frequency characteristics of images. However, traditional DWT suffers from several limitations, including the lack of phase information, shift variance, and limited directional selectivity, which can lead to distortions, misaligned edges, and loss of critical details in reconstructed medical images. This study proposes an enhanced DWT algorithm that addresses these limitations by integrating a trained Autoencoder for phase information preservation, implementing the Stationary Wavelet Transform (SWT) to mitigate shift variance, and employing a Directional Filter Bank (DFB) to improve directional selectivity. The proposed framework is evaluated using medical image datasets, including MRI, CT scans, and X-rays, with performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Reconstruction Error (MRE), and Coefficient Differences, Results demonstrate significant improvements in image quality, edge preservation, and compression efficiency, with up to 61.90% improvement in PSNR and reduced reconstruction error compared to traditional DWT. The enhanced algorithm ensures that critical diagnostic details are preserved, making it suitable for applications in medical imaging where accuracy and efficiency are paramount. This study contributes to the advancement of wavelet-based compression techniques, providing a robust solution for maintaining the integrity and diagnostic quality of medical images while reducing storage and bandwidth requirements.
942 ## - ADDED ENTRY ELEMENTS
Source of classification or shelving scheme
Item type Thesis/Dissertation
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Fund Source Total Checkouts Full call number Barcode Date last seen Price effective from Item type
          Filipiniana-Thesis PLM PLM Filipiniana Section 2025-10-24 donation   QA76.9 A43 C37 2025 FT8922 2026-01-07 2026-01-07 Thesis/Dissertation

© Copyright 2024 Phoenix Library Management System - Pinnacle Technologies, Inc. All Rights Reserved.