A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer's Disease

As the medical images contain both superficial and imperceptible patterns, textures are successfully used as discriminant features for the detection of cancers, tumors, etc. NEW METHOD: Our algorithm selects the specific image blocks and computes the textures using the following steps: At first, the center image slice of the axes (sagittal, coronal and axial) is divided into small blocks and those which approximately resembles the regions of interest are marked. Then, all the marked blocks which are in the same location as in the center slice are collected from all the other slices, and the textures are computed per block on all the individual slices. The generated textures are then pipelined to a feature selection algorithm with bootstrapping to pick-out features of high relevance and less redundancy and are exhaustively analyzed with multiple feature selection techniques like fisher score, elastic net, recursive feature elimination and classification algorithms like random forest, linear support vector machines, and k-nearest neighbors algorithms.

This method is validated on baseline MR images of 812 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results of binary classifications of different classes of Alzheimer's disease are also analyzed. The proposed features achieve the sensitivity/specificity of 89.58%/85.82% for AD/NC classification.

The proposed textures extraction runs over two times faster than other texture processing methods used for AD classification.

This study identifies the proposed textures with regional atrophies that could be used as potential checkpoints for Alzheimer's disease classification.