| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document Help

Title:Postopek brezizgubnega stiskanja razčlenjenih vokselskih podatkov
Authors:ID Špelič, Denis (Author)
ID Žalik, Borut (Mentor) More about this mentor... New window
ID Novak, Franc (Comentor)
Files:.pdf DR_Spelic_Denis_2011.pdf (9,33 MB)
MD5: 1B42BAD0A27EA889D97793DFA2F88121
PID: 20.500.12556/dkum/b3f04a2e-e08c-442c-bb66-a6b935307209
 
Language:Slovenian
Work type:Dissertation
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Doktorska disertacija obravnava področje stiskanja vokselskih podatkov. V uvodu naloge opredelimo problem stiskanja vokselskih podatkov, opišemo cilje naloge in podamo hipoteze, ki jih želimo preveriti. Uvodu sledi opis in definicija vokselskih podatkov, opis naprav, s katerimi podatke pridobivamo ter opis Hounsfieldove lestvice, s katero si lahko pomagamo pri razčlenitvi vokselskih podatkov. V nadaljevanju opišemo področje vokselske grafike Nalogo nadaljujemo s pregledom metod, ki se ukvarjajo s stiskanjem vokselskih podatkov. Opišemo dve metodi, ki sta bili objavljeni v zadnjem času, in podamo nekoliko podrobnejši opis metode QT-B. V jedru doktorske disertacije opišemo podporne tehnike, ki smo jih uporabili pri razvoju naše metode LoCoVox. Podrobneje opišemo standard JPEG-LS in standard JBIG. Sledi podrobnejši opis razvite metode LoCoVox ter njene spletne implementacije VoxelServer, VoxelClient in VoxelDecompressor. V predzadnjem poglavju lastnosti metode LoCoVox analiziramo z eksperimenti. Z metodo smo stisnili nabore vokselskih podatkov in rezultate primerjali s splošnonamenskima programoma ZIP in RAR ter z domenskospecifično metodo QT-B. Metoda je občutno boljša od metode QT-B in ZIP ter primerljiva z metodo RAR, v kolikor želimo prenesti celoten nabor podatkov. Če je dovolj, da prenesemo samo del podatkov (na primer, tkivo, tekočine, kosti), je metoda LoCoVox občutno uspešnejša.
Keywords:algoritmi, stiskanje podatkov, brezizgubno stiskanje, vokselski podatki, segmentacija
Place of publishing:Maribor
Publisher:[D. Špelič]
Year of publishing:2011
PID:20.500.12556/DKUM-18413 New window
UDC:004.925(043.3)
COBISS.SI-ID:14934806 New window
NUK URN:URN:SI:UM:DK:IOCBAWMJ
Publication date in DKUM:31.01.2012
Views:2632
Downloads:270
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share



Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Secondary language

Language:English
Title:Lossless compression of segmented voxel data
Abstract:The dissertation covers the area of voxel data compression. In the introduction we define the problem of compressing voxel data, describe the goals of the work and state the hypotheses we wish to confirm. The introduction is followed by the description and definition of voxel data, description of the devices for acquiring the data and description of Hounsfield’s scale for analyzing voxel data. In continuation we cover the area of voxel graphics and methods for visualization of voxel data. Next, an overview of methods for voxel data compression is given. We describe two methods that have been recently published and provide a more detailed description of the QT-B method. In the core of the dissertation we describe the support techniques that we applied at the development of our method LoCoVox. We describe in details the JPEG-LS and JBIG standards together with LoCoVox and its web implementation VoxelServer, VoxelClient, and VoxelDecompressor. The next chapter presents experimental verification of LoCoVox. We applied the method to compress voxel datasets and compared the results to the general-purpose compression algorithms ZIP and RAR and the domain-specific method QT-B. The main advantage of our method is that it allows transferring designated areas of data. The method is significantly improved compared to QT-B and ZIP and is comparable to the RAR method in case we want to transfer the complete dataset. If it is sufficient to transfer only a part of data (for instance tissue, liquids, bones), the LoCoVox is considerably more efficient.
Keywords:algorithms, data compression, lossless data compression, voxel data, segmentation


Comments

Leave comment

You must log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica