A Novel Approach for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's disease using MRI Images

作者：Ayub Amna; Farhan Saima^*; Fahiem Muhammad Abuzar; Tauseef Huma

来源：Advances in Electrical and Computer Engineering, 2017, 17(2): 113-122.

DOI：10.4316/AECE.2017.02015

• computer aided diagnosis
• feature extraction
• image analysis
• image classification
• pattern recognition

The main objective of our research is to introduce an approach that uses noninvasive MRI images to predict the conversion from mild cognitive impairment to Alzheimer's disease at an early stage. It detects normal controls that are likely to develop Alzheimer's disease and mild cognitive impairment patients that are likely to establish Alzheimer's disease within two years or, contrarily, their stage remains same. The proposed approach uses two types of features i.e. volumetric features and textural features. Volumetric features consist of volume of grey matter, volume of white matter and volume of cerebrospinal fluid. A total of 364 textural features have been calculated. To avoid the curse of dimensionality, textural features are reduced to 15 features using gain ratio, a ranking based search algorithm. All features are tested against four classifiers i.e. AODEsr, VFI, RBF and LBR. Leave-One-Out cross validation strategy is used for the evaluation of proposed approach. Results show accuracy of 98.33% with volumetric features and 100% with textural features using VFI and LBR. Our approach is innovative because of its higher accuracy results as compared to existing approaches yet with a smaller feature set.

• 出版日期2017