Computer-aided Diagnostics of Schizophrenia: Comparison of Different Feature Extraction Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F17%3A00113794" target="_blank" >RIV/00216224:14110/17:00113794 - isvavai.cz</a>

Výsledek na webu

<a href="http://acta.uni-obuda.hu/Radomir_Daniel_76.pdf" target="_blank" >http://acta.uni-obuda.hu/Radomir_Daniel_76.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.12700/APH.14.5.2017.5.12" target="_blank" >10.12700/APH.14.5.2017.5.12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computer-aided Diagnostics of Schizophrenia: Comparison of Different Feature Extraction Methods

Popis výsledku v původním jazyce

Receiving an early diagnosis of schizophrenia is a crucial step towards its treatment. However, in current thinking, the diagnosis is based on time-consuming criteria, burdened with subjectivity. Hence, objective and more reliable therapeutic tests are desirable for the clinical practice of Psychiatry. Since schizophrenia is characterized by progressive brain volume changes during the course of the disease, many studies have recently turned attention to machine learning and brain morphometric techniques serving as tools for computer-aided diagnosis of schizophrenia based on neuroimaging data. In our study, the methodology is applied to distinguish between 52 first-episode schizophrenia patients and 52 healthy volunteers on the basis of T1-weighted magnetic resonance images of their brains preprocessed by the means of voxel-based and deformation-based morphometry. The proposed classification schemes vary in the feature extraction and selection steps. Namely, Mann-Whitney testing is implemented as a simple univariate approach playing the role of a comparator to multivariate methods such as inter-subject PCA, the K-SVD algorithm, and pattern-based morphometry. The highest classification accuracy, 70%, is reached with the pattern-based morphometry technique. The study points out the difference between univariate and multivariate approaches towards neuroimaging data. Additionally, the contrast between feature extraction capabilities of voxel-based and deformation-based morphometry is demonstrated.

Název v anglickém jazyce

Computer-aided Diagnostics of Schizophrenia: Comparison of Different Feature Extraction Methods

Popis výsledku anglicky

Receiving an early diagnosis of schizophrenia is a crucial step towards its treatment. However, in current thinking, the diagnosis is based on time-consuming criteria, burdened with subjectivity. Hence, objective and more reliable therapeutic tests are desirable for the clinical practice of Psychiatry. Since schizophrenia is characterized by progressive brain volume changes during the course of the disease, many studies have recently turned attention to machine learning and brain morphometric techniques serving as tools for computer-aided diagnosis of schizophrenia based on neuroimaging data. In our study, the methodology is applied to distinguish between 52 first-episode schizophrenia patients and 52 healthy volunteers on the basis of T1-weighted magnetic resonance images of their brains preprocessed by the means of voxel-based and deformation-based morphometry. The proposed classification schemes vary in the feature extraction and selection steps. Namely, Mann-Whitney testing is implemented as a simple univariate approach playing the role of a comparator to multivariate methods such as inter-subject PCA, the K-SVD algorithm, and pattern-based morphometry. The highest classification accuracy, 70%, is reached with the pattern-based morphometry technique. The study points out the difference between univariate and multivariate approaches towards neuroimaging data. Additionally, the contrast between feature extraction capabilities of voxel-based and deformation-based morphometry is demonstrated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Polytechnica Hungarica

ISSN

1785-8860

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

HU - Maďarsko

Počet stran výsledku

16

Strana od-do

181-196

Kód UT WoS článku

000426127200012

EID výsledku v databázi Scopus

2-s2.0-85042332964