diplomsko delo
Ožbej Golob (Author), Aleksander Sadikov (Mentor)

Abstract

Standardni MRI podatki so večinoma kvalitativne narave (prikazujejo kontrast med različnimi tkivi). Tomografija električnih lastnosti (EPT MRI) je kvantitativna tehnika, kar pomeni, da za vsako slikovno točko prikaže konkretno vrednost. V diplomskem delu smo raziskali področje EPT MRI ter ugotovili, da je s pomočjo strojnega učenja na EPT MRI slikah možno zaznati prisotnost anomalije. Kot vhodne podatke smo uporabili EPT MRI slike možganov fantomov. V sklopu diplomskega dela smo napisali dve različici algoritma za zaznavanje anomalij. Klasičen pristop za zaznavanje anomalij v beli snovi zazna območja, ki jih ločujejo robovi, nato pa na podlagi srednje vrednosti električne prevodnosti območij določi, katera območja predstavljajo anomalijo. S klasičnim pristopom smo zaznali anomalije, ki so približne velikosti kocke z robovi, dolgimi 14 mm. Druga različica algoritma izkoristi kvantitativne karakteristike EPT MRI in anomalije zaznava preko absolutnih vrednosti električne prevodnosti. Preko absolutnih vrednosti smo zaznali anomalije, ki so približne velikosti kocke z robovi, dolgimi 12 mm.

Keywords

MRI;EPT MRI;zaznavanje anomalij;računalništvo in informatika;visokošolski strokovni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [O. Golob]
UDC: 004.85:611.012(043.2)
COBISS: 75999747 Link will open in a new window
Views: 286
Downloads: 34
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Other data

Secondary language: English
Secondary title: Anomaly detection in EPT MRI brain images of phantoms
Secondary abstract: Standard MRI data is mostly in a qualitative form (showing the contrast between different tissues). Electrical properties tomography (EPT MRI) is a quantitative technique, which means that it shows a specific value for each voxel. In this thesis we researched the field of EPT MRI and found that machine learning can be used to detect the presence of an anomaly in EPT MRI images. EPT MRI brain images of phantoms were used as input data. As part of the thesis, we wrote two versions of the anomaly detection algorithm. The classical approach for anomaly detection detects regions, separated by edges in white matter, and then determines which regions are an anomaly, based on the mean value of the electric conductivity. The classical approach has detected anomalies that are approximately the size of a cube with 14 mm long edges. The second version of the algorithm exploits the quantitative characteristics of EPT MRI and detects anomalies through absolute values of electrical conductivity. We have detected anomalies that are approximately the size of a cube with 12 mm long edges via absolute values.
Secondary keywords: MRI;EPT;machine learning;anomaly detection;computer science;computer and information science;diploma thesis;Strojno učenje;Anomalije (medicina);Možgani;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000470
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 63 str.
ID: 13328275