Brain Tumors: How Can Images and Segmentation Techniques Help?
Alejandro Veloz, Antonio Orellana, Juan Vielma, Rodrigo Salas F, Stéren Chabert · InTech eBooks · 2011
These days, cancer is one of the diseases that scares people the most. Brain cancer may be considered among the most difficult cancers to treat, as it involves the organ which is not only in control of the body, but is also responsible for the self-definition of the person. During surgery or any kind of treatment, eloquent areas must not be affected in order to minimize iatrogenic risks. Therefore good diagnosis and planning of treatment choices is essential. This is why images are now of paramount importance in the evaluation of brain tumors: oncologists, neurosurgeons and the entire medical team need to know how to understand them and how to use the current tools provided by computational techniques to take advantage of the information retrieved from them. A wide variety of images is available to support the physician’s actions at different levels, including diagnosis, treatment election, interventional support, and follow-up. Investigation in this area is very active; attempts are being made to go beyond the current pixel resolution, and to gain information with “molecular images”; not only in nuclear medicine but also in magnetic resonance images. Everybody agrees that images are now an invaluable service in the practice of medicine. However, the present and future use of images is intrinsically associated with larger numbers of images, which are not easily manageable by either radiologists or surgeons. Neuroradiology is conceived as a discipline in which the health status of a patient is inferred according to the visual inspection of images taken from different modalities. This implies that the success of the clinical diagnosis depends on the physician’s particular skills, and also on the information that the clinical team can handle. In addition, numerous image modalities are used frequently at different time points; therefore there is also a need for integration of the features reflected by these different sources of images. In order to provide support for this integration, automatic processing methods have been developed. Many Computer Aided Diagnostic (CAD) software packages have been developed, in particular to provide second readings in mammography, lung or brain cancer (Doi, 2007). These developments have motivated several clinical applications. Regarding brain tumor image processing, what is usually expected is to detect the localization and extension of the tumor, in other words to segment the tumor in the image.