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5 Journal of Medical Informatics & Technologies

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An attempt to improve Eigenface algorithm efficiency for colour images

100%

Orczyk T. , Porwik P.

2010

tom Vol. 16

201--207

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.

Instance based kNN modification for classification of medical data

80%

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

tom Vol. 24

99--106

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.

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

80%

Głowacki D. , Porwik P. , Orczyk T.

2010

tom Vol. 14

39--48

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.

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

80%

Orczyk T. , Porwik P. , Bernaś M.

Journal of Medical Informatics & Technologies

2014

tom Vol. 23

173--179

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.

Image analysis of immunohistochemical stains for detection of parathyroid disease

61%

Płaczek B. , Orczyk T. , Bułdak R. , Michalski M. , Segiet O. , Polaniak R.

tom Vol. 24

141--146

In this paper a method is introduced which enables automatic detection of parathyroid hyperplasia and parathyroid adenoma on the basis of immunohistochemical angiogenesis markers expression in micrographs. The proposed method uses digital image processing techniques and classification algorithms to detect diseased tissue. The disease detection is performed by classification of normalized color intensity histograms. Accuracy of this method was evaluated by using micrographs of parathyroid tissue sections obtained from patients that have undertaken surgery due to primary hyperparathyroidism. Use of different color models, various classifiers, and immunohistochemical markers was considered during the experiments. The experimental results show that the introduced method enables accurate detection of parathyroid disease. The most promising results were obtained for k-nearest neighbor and neural network classifiers.