Fuzzy Information Granulation of Medical Images. Blood Vessel Extraction from 3-D MRA Images

2002 ◽  
pp. 18-35 ◽  
Author(s):  
S. Kobashi ◽  
Y. Hata ◽  
L. O. Hall
Keyword(s):  
Blood Vessel ◽  
Medical Images ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Vessel Extraction ◽  
Fuzzy Information Granulation

FUZZY INFORMATION GRANULATION ON BLOOD VESSEL EXTRACTION FROM 3D TOF MRA IMAGE

2000 ◽  
Vol 14 (04) ◽  
pp. 409-425 ◽  
Author(s):  
SYOJI KOBASHI ◽  
NAOTAKE KAMIURA ◽  
YUTAKA HATA ◽  
FUJIO MIYAWAKI
Keyword(s):  
Image Segmentation ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Volume Data ◽  
Fuzzy Information ◽  
Vessel Extraction ◽  
Fuzzy Information Granulation

This paper shows an application of fuzzy information granulation (fuzzy IG) to medical image segmentation. Fuzzy IG is to derive fuzzy granules from information. In the case of medical image segmentation, information and fuzzy granules correspond to an image taken from a medical scanner, and anatomical parts, namely region of interests (ROIs), respectively. The proposed method to granulate information is composed of volume quantization and fuzzy merging. Volume quantization is to gather similar neighboring voxels. The generated quanta are selectively merged according to degrees for pre-defined fuzzy models that represent anatomical knowledge of medical images. The proposed method was applied to blood vessel extraction from three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) images of the brain. The volume data studied in this work is composed of about 100 contiguous and volumetric MRA images. According to the fuzzy IG concept, information correspond to the volume data, fuzzy granules corresponds to the blood vessels and fat. The qualitative evaluation by a physician was done for two- and three-dimensional images generated from the obtained blood vessels. The evaluation shows that the method can segment MRA volume data, and that fuzzy IG is applicable to, and suitable for medical image segmentation.

scholarly journals Fuzzy information granulation towards interpretable sentiment analysis

2017 ◽  
Vol 2 (4) ◽  
pp. 289-302 ◽  
Author(s):  
Han Liu ◽  
Mihaela Cocea
Keyword(s):  
Sentiment Analysis ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Fuzzy Information Granulation

A data-driven fuzzy information granulation approach for battery state of health forecasting

2020 ◽  
Vol 475 ◽  
pp. 228716 ◽  
Author(s):  
Wenjie Pan ◽  
Qi Chen ◽  
Maotao Zhu ◽  
Jie Tang ◽  
Jianling Wang
Keyword(s):  
Data Driven ◽  
State Of Health ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Fuzzy Information Granulation

Short-Term Wind Speed and Power Prediction Using Fuzzy Information Granulation-Support Vector Machine

2012 ◽  
Vol 608-609 ◽  
pp. 814-817
Author(s):  
Xiao Fu ◽  
Dong Xiang Jiang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Farm ◽  
Support Vector ◽  
Fuzzy Information ◽  
Short Term ◽  
Power Fluctuation ◽  
Data Set ◽  
Information Granulation ◽  
Fuzzy Information Granulation

The power fluctuation of wind turbine often causes serious problems in electricity grids. Therefore, short term prediction of wind speed and power as to eliminate the uncertainty determined crucially the development of wind energy. Compared with physical methods, support vector machine (SVM) as an intelligent artificial method is more general and shows better nonlinear modeling capacity. A model which combined fuzzy information granulation with SVM method was developed and implemented in short term future trend prediction of wind speed and power. The data, including the daily wind speed and power, from a wind farm in northern China were used to evaluate the proposed method. The prediction results show that the proposed model performs better and more stable than the standard SVM model when apply them into the same data set.

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