Insight Journal (ISSN 2327-770X)

The Insight Journal is an Open Access on-line publication covering the domain of medical image processing and visualization.

The unique characteristics of the Insight Journal include:

- Open-access to articles, data, code, and reviews
- Open peer-review that invites discussion between reviewers and authors
- Emphasis on reproducible science via automated code compilation and testing
- Support for continuous revision of articles, code, and reviews

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Multi-Atlas Brain MRI Segmentation with Multiway Cut

Sarikaya D., Zhao L., Corso J.J.

Characterization of anatomical structure of the brain and efficient algorithms for automatically analyzing brain MRI have gained an increasing interest in recent years. In this paper, we propose an algorithm that automatically segments the anatomical [...]

downloaded 1311 times, viewed 971 times and no review.

Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

Mahmood Q., Alipoor M., Chodorowski A., Mehnert1 A., Persson M.

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the [...]

downloaded 1005 times, viewed 703 times and no review.

Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images

R M.P., R S.S.K.

An automated method of MR Brain image segmentation is presented. A block based Expectation Maximization method is presented for the tissue classification of MR Brain images. The standard Gaussian Mixture Model is the most widely used method for MR Brain Image [...]

downloaded 811 times, viewed 715 times and no review.

Automatic Brain Tissue Segmentation of Multi-sequence MR Images Using Random Decision Forests

Pereira S., Festa J., Mariz J.A., Sousa N., Silva C.A.

This work is integrated in the MICCAI Grand Challenge: MR Brain Image Segmentation 2013. It aims for the automatic segmentation of brain into Cerebrospinal fluid (CSF), Gray matter (GM) and White matter (WM). The provided dataset contains patients with white [...]

downloaded 964 times, viewed 656 times and no review.

Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification

Van Opbroek A., Van der Lijn F., De Bruijne M.

We present a method for automated brain-tissue segmentation through voxelwise classification. Our algorithm uses manually labeled training images to train a support vector machine (SVM) classifier, which is then used for the segmentation of target images. The [...]

downloaded 1015 times, viewed 853 times and no review.

Gaussian Intensity Model with Neighborhood Cues for Fluid-Tissue Categorization of Multi-Sequence MR Brain Images

Katyal R., Paneri S., Kuse M.

This work presents an automatic brain MRI segmentation method which can classify brain voxels into one of three main tissue types: gray matter (GM), white matter (WM) and Cerebro-spinal Fluid (CSF). Intensity-model based classification of MR images has proven [...]

downloaded 773 times, viewed 688 times and no review.

MR Brain Segmentation using Decision Trees

Jog A., Roy S., Prince J.L., Carass A.

Segmentation of the human cerebrum from magnetic resonance images (MRI) into its component tissues has been a defining problem in medical imaging. Until recently, this has been solved as the tissue classification of the T1-weighted (T1-w) MRI, with numerous [...]

downloaded 874 times, viewed 875 times and no review.

MAP–Based Framework for Segmentation of MR Brain Images Based on Visual Appearance and Prior Shape

Alansary A., Soliman A., Khalifa F., Elnakib A., Mostapha M., Nitzken M., Casanova M., El-Baz A.

We propose a new MAP-based technique for the unsupervised segmentation of different brain structures (white matter, gray matter, etc.) from T1-weighted MR brain images. In this paper, we follow a procedure like most conventional approaches, in which [...]

downloaded 1088 times, viewed 795 times and no review.

Fully automatic brain segmentation using model-guided level sets and skeleton-based models

Wang C., Smedby �.

A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted [...]

downloaded 1179 times, viewed 751 times and no review.

Multi-Atlas-based Segmentation with Hierarchical Max-Flow

Rajchl M., Baxter J.S.H., Yuan J., Peters T.M., Khan A.R.

This study investigates a method for brain tissue segmentation from 3D T1 weighted (T1w) MR images via convex relaxation with a hierarchical ordering constraint. It employs a multi-atlas-based initialization from 5 training images and is tested on 12 T1w MR [...]

downloaded 1039 times, viewed 757 times and no review.

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