Division of Computer Engineering

Department of Electrical Engineering and Computer Sciences(EECS),
Graduate School of Engineering,

University of Hyogo

2167 Shosha Himeji, Hyogo, 671-2280, JAPAN

SOFT COMPUTING: Theory and Applications

Soft computing is a consortium of methodologies that exploit a tolerance for imprecision, uncertainty and partial truth. Results are achieved with tractability, robustness and a rapport with reality. These results are often better than that what is achieved through the exclusive use of conventional (hard) computing. The principal members of soft computing are for the most part synergistic and complementary. The main ideas of soft computing were brought forth by the confluence of fuzzy logic, neural networks, and probabilistic reasoning. Other ingredients include evolutionary algorithms, rough sets, chaos theory, belief networks and, although only partially, learning theory. In soft computing, each constituent contributes a distinct methodology for addressing problems in its domain. This is done in a complementary, rather than competitive way. Especially, I am now interested in realization of human reasoning, multiple-valued logic design and medical imaging applications.
click here for a definition of soft computing in greater detail

MEDICAL IMAGING Medical imaging has evolved at an explosive rate in the past few years. High-resolution, three-dimensional anatomical information can now be obtained in a routine manner with magnetic resonance imaging(MRI) and computer-aided tomography(CT). Three-dimensional functional imaging of blood flow and metabolic information can be obtained from positron emission tomography(PET) images, and functional four-dimensional electrophysiology (EEG) and evoked potential information can be obtained using high-speed computers.  In the context of medical imaging, soft computing technique appears as a power framework since it provides tools adapted to this task. Its main properties are:

1. It provides a way to represent and manipulate imprecise and uncertain information.
2. It will be able to demonstrate knowledge of Medical doctors.
3. It is well adapted to image processing since the natural spatial interpretation of soft computing leads to efficient representations of imprecise or implicit structures or classes in pictures.

Especially, a mixture of pattern recognition and SC-aided expert system techniques lead to successful tasks to segmentation of regions of interests and to give enhanced anatomical and functional resolution. This explosion of new techniques also emphasizes the need to integrate and focus the efforts of scientists and clinicians to facilitate communication, establish standards, and develop training programs..
click here for BISC special interest group in Medical Imaging(BISC-MED)

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