English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

evaluation. This task could also lead to new applications, including data compression, robust registration, and effective content based image retrieval in large medical databases. Accurate delineation of tumor can also be helpful for general modeling of pathological brains and the construction of pathological brain atlases Toga et al. (2001). Nevertheless, precise delineation of brain Tumor inMRI is a challenging problem that depends onmany factors. Indeed, there is a large class of tumor typeswhich vary greatly in size and position, have a variety of shape and appearance properties, have intensities overlapping with normal brain tissue, may deform and defect the surrounding structures giving an abnormal geometry also for healthy tissue. Moreover, MR images segmentation widely depends on the specific application and image modality. These images contain sometimes various amounts of noise and/or artifacts due to patient’s motion and soft tissue boundaries are sometimes not well defined. Traditionally manual brain tumors segmentation usually performed by marking the tumor regions slice-by-slice by human expert is time-consuming (hence impractical for processing large amounts of data), nonreproducible, difficult, and highly subjective. On the other hand, fully automatic and robust segmentation is highly required for clinical settings because it reduces significantly the computing time and generates satisfactory segmentation results. The existence of several MR acquisition protocols provides different information on the brain. Each image usually highlights a particular region of the tumor. In visualizing brain tumors, a second T1-weighted image is often acquired after the injection of a ’contrast agent’. These ’contrast agent’ usually contain an element whose composition causes a decrease in the T1 time of nearby tissue (gadolinium is one example) Brown & Semeka (2003). The presence of this type of ’enhancing’ area can indicate the presence of a tumor. Figure 1 illustrates an example of T1-weighted image before and after the injection of a contrast agent. Conventionally, it is difficult to segment a tumor by a simple technique like thresholding or classic edgedetection. These methods may not allow differentiation between non-enhancing tumor and normal tissue due to overlapping intensity distributions of healthy tissue with tumor and surrounding edema. Also, they are unable to exploit all information provided by MRI. Therefore, advanced image analysis techniques are needed to solve the problem. Various promising works have studied the tumor segmentation, offering a diversity of methods and evaluation criteria. In particular, pattern classification techniques refer to a Deformable Model-Based Segmentation of Brain Tumor from MR Images