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Biomedical subjects

Matti Pietikäinen

Publications and source records attributed to Matti Pietikäinen.

4 recordsLinked to original sources

Young adults with family history of coronary heart disease have increased arterial vulnerability to metabolic risk factors: the Cardiovascular Risk in Young Finns Study.

OBJECTIVE: Subjects with family history for coronary heart disease (CHD) may be more susceptible to the adverse effects of risk factors than subjects without family history. We investigated the occurrence of subclinical atherosclerosis in young adults with family history of CHD and tested the hypothesis that their arteries are more vulnerable to the proatherogenic effects of metabolic risk factors. METHODS AND RESULTS: Carotid artery intima-media thickness (IMT), carotid artery compliance (CAC), and brachial artery flow-mediated dilation (FMD) were measured in the 21-year follow-up of the Cardiovascular Risk in Young Finns Study in 2265 white adults 24 to 39 years of age. Subjects with positive family history of CHD had greater IMT compared with those with negative history (mean+/-SEM; 0.600+/-0.006 versus 0.578+/-0.002 mm; P=0.003, respectively). No differences were observed in CAC or FMD (both P>0.2). The difference in IMT remained similar after adjustment with current risk factors (P=0.008) or childhood risk factors measured 21 years earlier (P=0.002). The number of metabolic risk factors (components of the NCEP metabolic syndrome) correlated more strongly with IMT in subjects with family history of CHD than those without (P=0.007 for interaction). CONCLUSIONS: Young healthy adults with family history of CHD have increased carotid IMT. This is partly explained by their increased vulnerability to metabolic risk factors.

Adult↗

Face description with local binary patterns: application to face recognition.

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed.

Algorithms↗

A texture-based method for modeling the background and detecting moving objects.

This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.

Algorithms↗