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Widz S., Revett K., Ślęzak D.: A Rough Set-Based Magnetic Resonance Imaging Partial Volume Detection System.
Accepted to PREMI 2005 Conference, Kolkata, India.
Ten tekst dostępny jest tylko w języku angielskim.
Streszczenie. Segmentation of magnetic resonance imaging (MRI) data entails assigning tissue class labels to voxels. The primary source of segmentation error is the partial volume effect (PVE) which occurs most often with low resolution imaging - With large voxels, the probability of a voxel containing multiple tissue classes increases. Although the PVE problem has not been solved, the first stage entails correctly identifying PVE voxels. We employ rough sets to identify them automatically.
Widz S., Revett K., Ślęzak D.: A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts.
In Proceedings of the RSFDGrC'2005 Conference, Regina, Canada.
Ten tekst dostepny jest tylko w języku angielskim.
Streszczenie. We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89% for the noisiest image volume. We also tested the resultant classifier on real clinical data, which yielded an accuracy of approximately 84%
Widz S.: Rough Set Approach to Magnetic Resonance Brain Image Segmentation.
Master Thesis - Polish Japanese Institute of Information Technology, Warsaw, Poland
Ten tekst dostępny jest tylko w języku angielskim.
Streszczenie. Chapter Two introduses the Rough Set Theory principles, chapter Three presents the theory behind the Genetic Algorithms, chapter Four focuses on principles of Magnetic Resonance Imaging and finaly in chapters five and six we discus the different methods of MRI segmentation introducting the new approach based on Rough Set Theory (RST).
Widz S., Ślęzak D., Revett K.: Application of Rough Set Based Dynamic Parameter Optimization to MRI Segmentation.
In Proceedings of the NAFIPS'2004 Conference, Banff, Canada.
Ten tekst dostępny jest tylko w języku angielskim.
Streszczenie. We introduce a multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We use genetic algorithms to tune the parameterized attributes and search for the best segmentation models based on approximate reducts.
Widz S., Revett K., Ślęzak D.: An Automated Multi-spectral MRI Segmentation Algorithm Using Approximate Reducts.
In Proceedings of the RSCTC'2004 Conference, Uppsala, Sweden.
Ten tekst dostępny jest tylko w języku angielskim.
Streszczenie. We introduce an automated multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We utilized the T1, T2 and PD MRI images from the Simulated Brain Database as a 'gold standard' to train and test our segmentation algorithm. The results suggest that approximate reducts, used alone or in combination with other classification methods, may provide a novel and efficient approach to the segmentation of volumetric MRI data sets.
Janicki M., Ślęzak D.: Data mining w praktyce - Wprowadzenie w tematykę.
Tekst dostępny jest tylko w języku polskim.
Streszczenie. Niniejszy tekst przybliża tematykę związaną z analizą danych i odkrywaniem wiedzy w bazach danych ang. Knowledge Discovery in Databases (KDD). Ponadto omawiane są zagadnienia takie jak ekspoloracja danych czy hurtownie danych.
