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

• Published in: Journal of neuroscience methods Vol. 318; pp. 84 - 99
• Main Authors: Vaithinathan, Krishnakumar, Parthiban, Latha
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15-04-2019
• Subjects: Alzheimer's disease; Classification; Computer aided diagnosis; Texture; Computer aided diagnosis; Alzheimer's disease; Texture; Classification
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2019.01.011

•Novel texture extraction technique using different block sizes of the image slices.•Evaluation of multiple feature selection techniques and classifiers on 812 ADNI subjects.•Proposed textures show good characteristics for AD classification.•Texture extraction technique runs over two times faster than other texture processing methods.•Distribution of individual texture components for AD regional atrophies is analyzed.
As the medical images contain both superficial and imperceptible patterns, textures are successfully used as discriminant features for the detection of cancers, tumors, etc.
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.