Biomedical image and signal analysis

Biomedical image and signal analysis

The ongoing development of modern medical technologies, such as magnetic resonance imaging, positron-emission or computer tomography, stipulates the need for efficient and effective methods of biomedical data processing and recognition. In line with technological advancement, the shift of contemporary diagnostics towards personalized, predictive, preventive and participatory medicine can benefit from the recent achievements in image processing, computer vision and machine learning techniques. Algorithms and software designed in the Institute of Electronics address these issues in a variety of applications, like e.g. anatomical structures segmentation, vessels geometry modeling, blood flow and magnetic resonance angiography simulation, tissue perfusion quantification, skin lesion detection and categorization and finally diagnosis of voice disorders. Our scientific interests reach beyond medical applications. Automatic classification of barley grains species is an example of a project, where our competences in image segmentation, registration, and color and texture analysis have proven useful.

  • Image texture analysis

    Image texture analysis


    Image texture is a rich source of information about the objects visible in the image. This applies especially to biomedical images. Image texture visualized by means of various medical imaging modalities represents the properties of organs and tissues. Texture parameters reflect the physiological properties of such structures. This enables

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  • Analysis of laryngoscopic images

    Analysis of laryngoscopic images


    Voice is the basic interpersonal communication modality and is of particular importance in such professions as teacher, journalist or conference center employee. Early diagnosis of occupational voice disorders is becoming one of the health priorities. Current international standards emphasize the need for comprehensive voice assessment in phoniatric examinations. In

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  • Modeling and simulation of biophysical processes

    Blood flow pressure distribution and velocity streamlines in a renal vascularity model with stenosis in the renal artery.


    Medical images recognition involves consideration of multiple factors responsible for their formation. This knowledge is required because image brightness and contrast depend on both a diagnosed organ state, imaging parameters, as well as interaction between tissue and external signals, such as e.g. RF excitation signal. Moreover, in the case

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Institute Structure – Divisions

Medical Electronics Division

Head of the Division
prof. Piotr Szczypiński

Communications Division

Head of the Division
prof. Sławomir Hausman

Electronic Circuits and Thermography Division

Head of the Division
prof. Bogusław Więcek


Institute of Electronics
Lodz University of Technology
211/215 Wolczanska Str. B-9 building
PL 93-005 Lodz

+48 42 636 00 65
Mo-Fr: 8.00 - 16.00

VAT identification number: PL 727-002-18-95