SEARCH RESULT

Image reconstruction is an intrinsic part of nuclear imaging and is achieved by using various image reconstruction algorithms. Over the past years, various reconstruction algorithms have been developed with various image quality parameters that affect the diagnosis quality of the resultant image. Image reconstruction is performed using mathematical algorithms like FBP, MLEM, OSEM, OE, SOE and LM-OSEM etc. These reconstruction algorithms have various closely related image quality parameters associated with them that constitute to a good or bad image. Some of these image quality parameters include image noise, contrast resolution, specificity, sensitivity, accuracy, iteration, SNR, CNR, general image quality, sharpness, blurriness as well as time etc. Nuclear radiologists are faced with the problem of deciding which algorithm is best for a particular diagnosis due to the close relation between these parameters. Incorporation of decision making theories in to image reconstruction algorithms can be of great ease and accuracy in nuclear imaging for specific diagnosis. This project seeks to evaluate and compare some of the image quality parameters of some reconstruction algorithms using multi criteria decision making technique or theories to ease the process of decision making in Nuclear medicine imaging. Our decision theory of interest is fuzzy PROMETHEE. The PROMETHEE technique is a multi-criteria desicion making technique developed by Brans et al. (1984, 1986) work on the priciple of mutually comparing related alternatives with regards to their related and selected criteria. The advantages of PROMETHEE model is seen on it efficeincy and easiness in conception and application compared to other MCDM methods. Fuzzy logic on the otherhand is a form of multi valued logic that allow intermediate values in form of multi valued logic which the truth values of variables maybe any number between 0 and 1 but conceptually distinct due to different interpretations, where binary sets have true or false valued logic. Fuzzy logic variables may have a truth values that ranges in degree, where the truth values can range between completely true and completely false. Fuzzy logic is applied to improve the efficiency and simplicity of the design process. There have been very few research based on the approach of fuzzy PROMETHEE (F-PROMETHEE). In the real life conditions, most times we are not able to collect crisp data to define a problem properly and make an optimal decision. Using Fuzzy sets allows the decision maker to define the problem under the vague condition which is more realistic. The main aim of the Fuzzy PROMETHEE model was proposing a comparison between two fuzzy sets. For this aim, Yager (1981) found an index which is determined with the center of weight of the surface of the membership function to compare the fuzzy numbers. Yager (1981) define the magnitude of a triangular fuzzy numbers ???? ̃ = (????, ????, ????) corresponding to center of triangule with the YI=(3n-a+b)/3 formula. In our F- PROMETHEE application we will apply to Yager index.

International Symposium on Industry 4.0 and Applications
ISIA

Dilber Uzun Özşahin Nuhu Abdulhaqq Isa Berna Uzun Ashghan Abu Farah İlker Özşahin