Brain tissue segmentation examples, using the three-level threshold.

<p>Each column shows the result of segmentation for a brain image with different lesion load. (a) Shows the original brain images. (b) The obtained member functions plots. (c) Shows the segmentation results using the three-level thresholding (maximum fuzzy entropy approach). (d) Dark membership images. (e) Medium membership images. (f) Bright membership images. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)</p

Mean values of lesion volumes (LV), similarity criteria and mean value of segmentation time (T) for each patient data and for all images in data set (last line of the table) obtained using the proposed method.

<p>Mean values of lesion volumes (LV), similarity criteria and mean value of segmentation time (T) for each patient data and for all images in data set (last line of the table) obtained using the proposed method.</p

Block diagram of the proposed approach for fully automatic segmentation of MS lesions.

<p>Block diagram of the proposed approach for fully automatic segmentation of MS lesions.</p

A typical Infratentorial lesion.

<p>(a) PD-w, (b) T2-w, (c) and FLAIR images of a patient with remitting relapsing multiple sclerosis (RRMS) demonstrate a pontine lesion (arrows) that is not demonstrated on the FLAIR sequence <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065469#pone.0065469-Sahraian1" target="_blank">[2]</a>.</p

The average of the Jaccard Scores for different values of the BM parameter (in the interval [0.01, 0.1]).

<p>The average of the Jaccard Scores for different values of the BM parameter (in the interval [0.01, 0.1]).</p

TP, TF, FP, and TN values are shown based on comparison between segmented regions by proposed method (Automatic segmentation) and manual segmentation.

<p>TP, TF, FP, and TN values are shown based on comparison between segmented regions by proposed method (Automatic segmentation) and manual segmentation.</p

Similarity index (SI) values for the proposed method and the other methods.

<p>s: slices, v: volume.</p><p>The reader is referred to the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065469#pone.0065469-Llad1" target="_blank">[9]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065469#pone.0065469-Mortazavi1" target="_blank">[58]</a> for complete explanations about methods reported here.</p

Segmentation of CSF areas.

<p>(a) Shows a typical brain image. (b) Dark membership image (to give more understanding, the obtained image has been inverted). (c) Result of applying the localized-weighted filter to dark membership image (the inverted result). (d) : CSF areas obtained from filtered dark membership image. (e) : CSF areas obtained from dark membership image. (g) Result of CSF segmentation. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).</p

Contrast-Enhanced FLAIR image for segmentation of MS lesions.

<p>(a) Shows a typical brain image. (b) Histogram of original brain image. (c) Contrast-Enhanced image. (d) Histogram of Contrast-Enhanced image. (e) : Lesion areas obtained from Contrast-Enhanced FLAIR image. (f) : All candidate MS lesions obtained from bright membership image. (g) Result of MS lesions segmentation. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).</p

Brain tissue segmentation examples.

<p><b>Each column shows the result of the proposed method for a brain image with different lesion load.</b> (a) Original brain images. (b) Rresult of automatic segmentation. (c) Results of the automatic MS lesion segmentation overlaid on brain image. (d) Extracted lesions. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).</p