Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Epigenetics of human diseases and scope in future therapeutics

علم التخَلُّق هو دراسة التعديلات النوكليوتيدية المورَّثة التي تعمل كآلية تنظيمية دون تغيير التسلسل النيوكليوتيدي للجينوم. تؤثر إشارات خارجية مثل البيئة، ونمط الحياة، والتغذية، والإجهاد والعوامل النفسية على آليات التخَلُّق. وتتوافق هذه الآلية مع المعلومات الجينية التي تلعب دورا مهما في حياة الفرد قبل الولادة وبعدها. لقد كشفت الدراسات الأخيرة في علم التخَلُّق إمكانات علم التخَلُّق في توضيح آليات أمراض مختلفة لم يتم فهمها سابقا بشكل كامل. ناقشنا في هذا الاستعراض آليات التخَلُّق الأساسية ودورها في الصحة والمرض. إضافة إلى ذلك فقد تم وصف الانحرافات في التنظيم التخلُّقي التي تم تسجيلها لبعض الأمراض البشرية الشائعة. وأخيرا، تتناول الورقة بعض الأساليب في علم التخَلُّق، التي تكمن بها القدرة على العلاج الموجَّه للأمراض

Color representation of T2 brain MR image with our proposed colorization method.

<p>(A) Abnormal T2 brain MR image of patient aged 32. (B) Color transformed image. (C) Selected centroids with proposed method.</p

Comparison and Jaccard Coefficient of GM, WM and CSF in pixels (Avg).

<p>Comparison and Jaccard Coefficient of GM, WM and CSF in pixels (Avg).</p

Color Image from which the 200 color derived (courtesy of [44]).

<p>Color Image from which the 200 color derived (courtesy of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033616#pone.0033616-Plataniotis1" target="_blank">[44]</a>).</p

Comparison among the results obtained with our proposed segmentation method and other standard segmentation methods using the image obtained with Siemens MRI scanner.

<p>(A) T2 brain MRI image with Siemens MAGNETOM Aera 1.5 T <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033616#pone.0033616-Siemens1" target="_blank">[52]</a> with dimension 600×600. (B) Segmentation with proposed method. (C) Watershed segmentation. (D) Gaussian classifier segmentation using Analyze 10 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033616#pone.0033616-AnalyzeDirect1" target="_blank">[50]</a>.</p

Color transformed T2 brain MR image.

<p>(A) Gray scale brain MR image. (B) Colorized brain MR image obtained using proposed method.</p

Comparison among the colorized results of our proposed method and MCMxxxVI [<b>35</b>].

<p>(A) T1 image. (B) T2 image. (C) The fused color image is obtained with MCMxxxVI. (D)–(E) Representing the color version of images (A)–(B) respectively, produced by our proposed method.</p

Comparison among the results obtained with our proposed segmentation method and EM-based segmentation.

<p>(A) T1 brain MR image of 2 year old child. (B) Segmented image with EM segmentation method. (C) Segmentation by proposed method.</p