Wyniki wyszukiwania - BazTech

1

An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

Doroz R., Wrobel K., Porwik P.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 1

209--225

EN

This paper presents an effective method for the detection of a fingerprint’s reference point by analyzing fingerprint ridges’ curvatures. The proposed approach is a multi-stage system. The first step extracts the fingerprint ridges from an image and transforms them into chains of discrete points. In the second step, the obtained chains of points are processed by a dedicated algorithm to detect corners and other points of highest curvature on their planar surface. In a series of experiments we demonstrate that the proposed method based on this algorithm allows effective determination of fingerprint reference points. Furthermore, the proposed method is relatively simple and achieves better results when compared with the approaches known from the literature. The reference point detection experiments were conducted using publicly available fingerprint databases FVC2000, FVC2002, FVC2004 and NIST.

2

Feature projection k-NN classifier model for imbalanced and incomplete medical data

Porwik P., Orczyk T., Lewandowski M., Cholewa M.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

644--656

EN

Many datasets, especially various historical medical data are incomplete. Various qualities of data can significantly hamper medical diagnosis and are bottlenecks of medical support systems. Nowadays, such systems are often used in medical diagnosis. Even great number of data can be unsuitable when data is imbalanced, missing or corrupted. In some cases these troubles can be overcome by machine learning algorithms designed for predictive modeling. Proposed approach was tested on real medical data and some benchmarks dataset form UCI repository. The liver fibrosis disease from a medical point of view is difficult to treatment and has a significant social and economic impact. Stages of liver fibrosis are diagnosed by clinical observation and evaluations, coupled with a so-called METAVIR rating scale. However, these methods may be insufficient, especially in the recognition of phase of the disease. This paper describes a newly developed algorithm to non-invasive fibrosis stage recognition using machine learning methods – a classification model based on feature projection k-NN classifier. This solution allows extracting data characteristics from the historical data which may be incomplete and may contain imbalance (unequal) sets of patients. Proposed novel solution is based on peripheral blood analysis without using any specialized biomarkers, and can be successfully included to medical diagnosis support systems and might be a powerful tool for effective estimation of liver fibrosis stages.

3

User verification based on the analysis of keystrokes while using various software

Wesołowski T. E., Porwik P.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

13--22

EN

The article presents the new approach to a computer users verification. The research concerns an analysis of user’s continuous activity related to a keyboard used while working with various software. This type of analysis constitutes a type of free-text analysis. The presented method is based on the analysis of users activity while working with particular computer software (e.g. text editors, utilities). A method of computer user proﬁling is proposed and an attempt to intrusion detection based on k-NN classifier is performed. The obtained results show that the introduced method can be used in the intrusion detection and monitoring systems. Such systems are especially needed in medical facilities where sensitive data are processed.

4

Personal identity verification method based on lips photographs

Wrobel K., Doroz R., Porwik P., Naruniec J., Kowalski M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

59--65

EN

The paper presents a personal identification method based on lips photographs. This method uses a new approach to the extraction and classification of characteristic features of the mouth from photographs. It eliminates the drawbacks that occur during the acquisition of lip print images with the use of the forensic method that requires special tools. Geometrical dimensions of the entire mouth as well as of the upper and lower lips were adopted as the features, on the basis of which the verification is performed. An ensemble classifier was used for the classification of the features obtained. The effectiveness of the classifier has been verified experimentally.

5

Person verification based on keystroke dynamics

Doroz R., Porwik P., Safaverdi H.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

39--44

EN

This paper presents a new multilayer ensemble of classifiers for users verification who use computer keyboard. The special keyboard extracts the key pressure and latency between keyboard keys pressed during password entered. When user is typing password the system creates a pattern based on time and key pressure. For users verification group of classifiers have been proposed. It allows to obtain the higher accuracy level compared to alternative techniques. The efficiency of the proposed method has been confirmed in the experiments carried out.

6

Instance based kNN modification for classification of medical data

Orczyk T., Porwik P., Lewandowski M., Cholewa M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

99--106

EN

Paper describes a novel modification to a well known kNN algorithm, which enables using it for medical data, which often is a class-imbalanced data with randomly missing values. Paper presents the modified algorithm details, experiment setup, results obtained on a cross validated classification of a benchmark database with randomly removed values (missing data) and records (class imbalance), and their comparison with results of the state of the art classification algorithms.

7

Smart selection of signal analysis algorithms for telecare of high-risk pregnancy

Wróbel J., Matonia A., Horoba K., Jeżewski J., Czabański R., Pawlak A., Porwik P.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

27--33

EN

Telemedical system for fetal home monitoring with smart selection of signal analysis algorithms is presented in this paper. Fetal monitoring signals are provided by a mobile instrumentation consisting of bioelectrical signal recorder and tablet PC which retrieves and processes the data as well as provides wireless data transmission based on Internet. The fetal surveillance system enables analysis, dynamic presentation and archiving of acquired signals and medical data. Novelty of the proposed approach relies on smart fitting of the algorithms for analysis of the abdominal signals in mobile instrumentation, as well as on controlling of the fetal monitoring session from the surveillance center. These actions are performed automatically through continuous analyzing of the signal quality and the reliability of the quantitative parameters determined for the acquired signals. Using that approach the amount and content of data transmitted through remote channels to the surveillance center can be controlled to ensure the most reliable assessment of the fetal well-being.

8

Medical diagnosis support system based on the ensemble of single-parameter classifiers

Orczyk T., Porwik P., Bernaś M.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

173--179

EN

This paper presents a medical diagnosis support system based on an ensemble of single parameter k–NN classifiers [1]. System was verified on a database containing real blood test results of diagnosed patients with a liver fibrosis. This dataset contains problems typical to a real medical data – especially missing values. Paper also describes the process of selecting a subset of parameters used for further evaluation (feature selection/elimination algorithm). Complete database contains many parameters, but not all are important for diagnosis, thus eliminating them is an important step. A comparison of proposed method of classification and feature selection with methods known from literature has also been presented.

9

Diagnosing Parkinson’s disease using the classification of speech signals

Froelich W., Wróbel K., Porwik P.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

187--193

EN

This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification process based on majority criterion is performed. The advantage of using our new approach is the resulting, reliable patientoriented medical diagnose. The proposed two-step method of classification allows also to deal with the variable number of voice samples gathered for every patient. Preliminary experiments revealed quite satisfactory classification accuracy obtained during the performed leave-one-out cross validation.

10

Design challenges for home telemonitoring of pregnancy as a medical cyber-physical system

Horoba K., Jeżewski J., Wróbel J., Pawlak A., Czabański R., Porwik P., Penkala P.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

59--66

EN

The paper introduces the problem of designing a telemedical system for pregnancy monitoring at home. It focuses on design challenges concerning embedded computing and networking, requirements modelling, and presents the architecture and solutions when based on new class Medical Cyber-Physical Systems (MCPS). The proposed system consists of a Body Area Network (BAN) of advanced sensors that are interconnected on a body of a pregnant woman, a Personal Area Network (PAN) that is responsible for embedded processing of physical signals, smart alarms, data transmission and communication with the Surveillance Centre located in hospital. It is expected that this dependable telemedical system will provide a high societal value to women with high-risk pregnancy.

11

Influence of missing data imputation method on the classification accuracy of the medical data

Orczyk T., Porwik P.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

111--116

EN

Aim of this study is to show the dangers of filling missing data - particularly medical data. Because there are many dedicated medical expert systems and medical decision support systems, a special attention must be paid on the construction of classifiers. Medical data are almost never complete, and completion of the missing data requires a special care. The safest approach of dealing with missing data would be removing records with missing parameters and/or removing parameters that are missing in the records. Unfortunately reducing data set that is already very small is not always an option. Dangers coming out from data imputation are shown in the article, which presents the influence of selected missing data filling algorithms on the classification accuracy.

12

The use of methods of statistical analysis in signature recognition system based on Levenshtein distance

Pałys M., Doroz R., Porwik P.

Journal of Medical Informatics & Technologies

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2012

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Vol. 21

67--73

EN

The study being presented is a continuation of the previous studies that consisted in the adaptation and use of the Levenshtein method in a signature recognition process. Three methods based on the normalized Levenshtein measure were taken into consideration. The studies included an analysis and selection of appropriate signature features, on the basis of which the authenticity of a signature was verified later. A statistical apparatus was used to perform a comprehensive analysis. The independence test ◈ was applied. It allowed determining the relationship between signature features and the error returned by the classifier.

13

Classification techniques for non-invasive recognition of liver fibrosis stage

Krawczyk B., Woźniak M., Orczyk T., Porwik P., Musialik J., Błońska-Fajfrowska B.

Journal of Medical Informatics & Technologies

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2012

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Vol. 20

121--127

EN

Contemporary medicine should provide high quality diagnostic services while at the same time remaining as comfortable as possible for a patient. Therefore novel non-invasive disease recognition methods are becoming one of the key issues in the health services domain. Analysis of data from such examinations opens an interdisciplinary bridge between the medical research and artificial intelligence. The paper presents application of machine learning techniques to biomedical data coming from indirect examination method of the liver fibrosis stage. Presented approach is based on a common set of non-invasive blood test results. The performance of four different compound machine learning algorithms, namely Bagging, Boosting, Random Forest and Random Subspaces, is examined and grid search method is used to find the best setting of their parameters. Extensive experimental investigations, carried out on a dataset collected by authors, show that automatic methods achieve a satisfactory level of the fibrosis level recognition and may be used as a real-time medical decision support system for this task.

14

The attempt of the blood vessel contractibility estimation on the basis of the computed tomography imaging

Porwik P., Sosnowski M., Wróbel K., Wesołowski T.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

173--181

EN

Cardiovascular mortality remains a leading health and social problem in many countries throughout the world. Its main cause is related to atherosclerosis of coronary and cerebral vessels with their most severe consequences: heart attack and stroke. Therefore, it is obvious that current preventive measures include early detection of atherosclerosis process. Multi-detector computed tomography (MDCT) is one of imaging modalities allowing for noninvasive detection of atherosclerotic lesion within coronary arteries in subjects with accumulation of risk factors (smoking, high lipids, hypertension, male gender, family history) or with suspicion of coronary artery disease (CAD). In is very important that the tomographic images are taken in synchronization with cardiac cycle so that, during few heartbeats, an appropriate series of images can be recorded. Commonly, cardiac MDCT is used for visualization of cardiac and vessels morphology. Heart function can also be determined, however, this MDCT potential is only rarely applied, as current echocardiographic modalities are sufficient. Functional analysis of coronary arteries (flow, reserve) is usually approached by means of invasive procedures. We aimed at finding solution for evaluation of another kind of functional analysis of coronary arteries, namely vessel's wall compliance by means of MDCT coronary angiography. Under the proposed procedure, on basis of serial CT images of the vessels over entire cardiac cycles, the internal area of the blood vessel is measured and its changes during various phases of heartbeat (systole, diastole) are calculated. If the vessel wall has been changed by atherosclerotic plaque, either calcified or non-calcified, then its compliance will be reduced due to its stiffness. Calculation of coronary artery compliance requires a series of measurements, which is unreliable and impractical for doing manually. One component of the method described herein involves the images being converted into binary representations and the Hough Transform then applied. The overall methodology proposed in this paper assists in the preparation of a medical diagnosis.

15

Simple and non-invasive liver fibrosis stage prediction method

Orczyk T., Pałys M., Porwik P., Musialik J., Błońska-Fajfrowska B.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

227--231

EN

In this paper a simple and non-expensive indirect fibrosis stage prediction method is described. Presented method is non-invasive and is based on the results of the generic blood tests. The method is based on a statistical analysis of wide range of blood tests results supported with the experience of hepatologists.

16

The pixel alignment based algorithm for continuous orientation field estimation

Więcław Ł., Porwik P.

Journal of Medical Informatics & Technologies

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2010

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Vol. 15

47--52

EN

This paper presents a new estimation method of fingerprint orientation field. An accurate estimation of fingerprint orientation fields is an essential step in automatic fingerprint recognition systems (AFIS). Most popular, gradient-based method is very sensitive to noise (image quality). Proposed algorithm is a modification of, more resistant to noise, mask-based method, which provides orientation limited to discrete values. This modification is based on aggregation of pixel values differentiation and was used to more precise estimation of magnitude of orientation vectors. This approach allows to obtain a continuous values of orientation field still maintaining robust to noise.

17

The new rule based colour classifier in the problem of human skin colour detection

Głowacki D., Porwik P., Orczyk T.

Journal of Medical Informatics & Technologies

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2010

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Vol. 14

39--48

EN

This article presents new rules, which can be used to construct a classifier for image areas segmentation. Segmentation is made on upon the colours, which are commonly associated with human skin colour. The new rules of this classifier have been developed on the basis of the analysis and modifications of two other classifiers, which has been described in the literature. Nowadays, such classifiers are commonly used in practice: in photographic equipment, photo-editing software, biological images analysis or in-room person detecting systems.

18

The lip print recognition using Hough transform

Smacki Ł., Porwik P., Tomaszycki K., Kwarcińska S.

Journal of Medical Informatics & Technologies

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2010

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Vol. 14

31--38

EN

This paper presents the new method of lip print recognition. Lip print analysis is based on sulcus-topology features manifested on human lips. The structure of the characteristic lip lines is transformed to a digital image and classified using the Hough transformation. The proposed algorithm gives good level of recognition accuracy and can be used in biometric applications and applied in forensic services.

19

Biometric recognition system based on the motion of the human body gravity centre analysis

Porwik P., Zyguła J., Doroz R., Proksa R.

Journal of Medical Informatics & Technologies

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2010

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Vol. 15

61-69

EN

In this paper we present a novel approach that enables the determination and measurement of important features associated with the human body movement. This information can be used in the construction of a biometric personal identification system. Biometrics is, essentially, a pattern recognition system based on measurements of unique physiological or behavioural features as acquired from an individual. The domain of biometric techniques is currently placed within recently developed disciplines of science. Biometry or biometrics is simply defined as automatically recognizing a person using distinguishing traits and is widely used in various security systems. Biometry can be defined as a method of personal identification based on individuals' physical and behavioural features. Physiological biometrics covers data coming directly from a measurement of part of a human body, for example a fingerprint, the shape of the face, or from the retina. Behavioural biometrics analyses data obtained on the basis of an activity performed by a given person, for example speech and the handwritten signature. The system of biometrics defined above can now be expanded, and a new biometrics system can be considered. In our approach, human foot pressure on a surface is measured and the pressure data retrieved. The pressure parameters are collected without the necessity of any movements of the feet.

20

An attempt to improve Eigenface algorithm efficiency for colour images

Orczyk T., Porwik P.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

201--207

EN

This article presents an attempt to improve Eigenface algorithm efficiency by using image pre–filtering in order to eliminate background areas of the picture and illumination influence. The background is treated as noise, so when noise is present then efficiency of the algorithm decreases. In order to eliminating this inconvenience, analysed image is pre–filtered by means of the colour classifier. The classifier eliminates pixels which have different colour than an average human skin colour on a digital photo. This causes that the Eigenface algorithm is less sensitive to background noise. The illumination influence was minimized by using hue information instead of traditionally used luminance. The main advantage of the proposed approach is possibility of using in environments where diverse image background texture and scene illumination appears. The eigenfaces technique can be applied in handwriting analysis, voice recognition, hand gestures interpretation and medical imaging.