Comparison of different candidate extractors on ROC training set.

Comparison of different candidate extractors on ROC training set.</p

The results of the proposed method and the DRSCREEN approach on the DRDB database.

<p>The results of the proposed method and the DRSCREEN approach on the DRDB database.</p

An example of color fundus image containing MAs.

<p>(A) The color fundus image. (B) The corresponding enlarged part of (A) in green channel with indicated MAs (diamond: regular MA, circle: subtle MA, triangle: irregular MA, square: clustered MAs, pentagon: MA close to vessel). Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161556#pone.0161556.ref008" target="_blank">8</a>] under a CC BY license, with permission from Dr. Yalin Zheng, original copyright 2012.</p

The sensitivities of cross validation on ROC training set with different <i>T</i> in RUSBoost classifier.

<p>The sensitivities of cross validation on ROC training set with different <i>T</i> in RUSBoost classifier.</p

The process of map computation.

<p>(A) The preprocessed input image. (B)-(C) The log condition number maps with different support regions. (D) The final map. (E) An image patch of preprocessed image contains 5 true MAs marked with ‘□’. (F) The corresponding patch of map.</p

FROC curves of the proposed method and the DRSCREEN approach on the DRDB database.

<p>The FROC curve reproduced from the original work of DRSCREEN [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161556#pone.0161556.ref025" target="_blank">25</a>] on the same database.</p

The result of candidate MAs localization.

<p>(A) The map. (B) The final location of candidates MAs, where all candidates indicated with ‘×’ and the true MAs provided by medical expert marked with ‘□’.</p

Examples of error detection.

<p>(A)-(B) The true MAs with low contrast. (C) The vessel crossing with high contrast. (D)-(E) The non-MAs have very similar appearance as true MAs.</p

Segmentation results of candidate MAs.

(A), (C) and (E) The image patches containing MAs. (B), (D) and (F) The segmentation results.</p

Different distributions of gradient vectors of the vessel-like and the MA-like objects.

<p>(A) The gradient field of a vessel-like object. (B) The distribution of gradient vectors in (A). (C) The gradient field of a MA-like object. (D) The distribution of gradient vectors in (C).</p