3D volume is represented by 2D features.

<p>The brain is sliced along 3 orientations to obtain 2D images, and SIFT features are extracted from 2D images.</p

Classification results for different cell block size.

<p>Classification results for different cell block size.</p

Classification results in the literature: SIFT-based methods.

<p>Classification results in the literature: SIFT-based methods.</p

Flow diagram of proposed approach.

<p>The overall process contains the following steps: (1) the training MR images are aligned to template brain; (2) keypoints are extracted for every subject and matched among different subjects; (3) HOG descriptors are calculated for keypoints, and differences in terms of HOG is quantified. The effect of keypoint in classification is represented through assigning scores for them. (4) based on keypoints and their scores, testing subject is classified.</p

Illustration of the extraction of local feature (HOG).

<p>Gradient magnitudes and orientations are sampled, and then accumulated into orientation histogram with 8 channels. (a) Cell block. (b) 8 histogram channels. (c) Oriented gradients matrix.</p

Illustration of some keypoints with a high score.

<p>Four keypoints are listed, which appear in the same anatomical structure of left brain and right brain with a high score.</p

Classification results with or without the use of SIFT features.

<p>Classification results with or without the use of SIFT features.</p

Classification accuracy with different numbers of keypoints.

<p>As the number of keypoints increases, the classification accuracy changes in AD-86, AD-126, PD-46 and PD-212.</p

Classification results for different score methods.

<p>Classification results for different score methods.</p