Rough Sets in Medical Informatics Applications

Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative ‘youth’ compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.

[1]  Roman Podraza,et al.  Ignoring improper data in decision support system for medical applications , 2004, Ann. UMCS Informatica.

[2]  Sankar K. Pal,et al.  Granular computing, rough entropy and object extraction , 2005, Pattern Recognit. Lett..

[3]  D. Ślęzak,et al.  Application of rough set based dynamic parameter optimization to MRI segmentation , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[4]  Chun-Bao Chen,et al.  Rough Set-Based Clustering with Refinement Using Shannon's Entropy Theory , 2006, Comput. Math. Appl..

[5]  Xian-Ming Huang,et al.  A new application of rough set to ECG recognition , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[6]  Jörg H. Siekmann,et al.  Artificial Intelligence and Soft Computing - ICAISC 2004 , 2004, Lecture Notes in Computer Science.

[7]  Gerald Schaefer,et al.  An overview of rough-hybrid approaches in image processing , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[8]  Shusaku Tsumoto,et al.  Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model , 2004, Inf. Sci..

[9]  Alicja Wakulicz-Deja,et al.  Applying Rough Set Theory to Multi Stage Medical Diagnosing , 2003, Fundam. Informaticae.

[10]  James V. Stone.  Independent Component Analysis: A Tutorial Introduction , 2007 .

[11]  Dominik Ślęzak,et al.  Various approaches to reasoning with frequency based decision reducts: a survey , 2000 .

[12]  Aapo Hyvärinen,et al.  Independent Component Analysis: A Tutorial , 1999 .

[13]  James F. Peters,et al.  K-means Indiscernibility Relation over Pixels , 2004, Rough Sets and Current Trends in Computing.

[14]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[15]  K. Kondo,et al.  Rough sets based medical image segmentation with connectedness , 2004, Proceedings World Automation Congress, 2004..

[16]  Adam Mrózek,et al.  Rough sets in hybrid methods for pattern recognition , 2001 .

[17]  Madhuchhanda Mitra,et al.  A Rough-Set-Based Inference Engine for ECG Classification , 2006, IEEE Transactions on Instrumentation and Measurement.

[18]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[19]  Andrzej Skowron,et al.  Independent Component Analysis, Princpal Component Analysis and Rough Sets in Hybrid Mammogram Classification , 2006, IPCV.

[20]  Ajay K. Ray,et al.  Fusion of rough set theoretic approximations and FCM for color image segmentation , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[21]  Adam Mrózek,et al.  Rough sets in hybrid methods for pattern recognition , 2001, Int. J. Intell. Syst..

[22]  Aboul Ella Hassanien,et al.  Detection of Spiculated Masses in Mammograms Based on Fuzzy Image Processing , 2004, ICAISC.

[23]  Andrzej Skowron,et al.  Rough Sets , 1995, Lecture Notes in Computer Science.

[24]  Zbigniew M. Wojcik,et al.  Rough approximation of shapes in pattern recognition , 1987, Comput. Vis. Graph. Image Process..

[25]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.