Technology Automatically Delimits Areas of Brain in Medical Images

Researchers have developed a way to enhance the delimitation of tumors in medical images.

As the investigators, from NUP/UPNA-Public University of Navarre (Spain), explained, “when the doctor decides where tumor tissue should be separated from healthy tissue, our algorithm ensures that he/she is never going choose the worst option because the best solution is automatically offered.”

The study’s findings were submitted by the Artificial Intelligence and Approximate Reasoning Group (GIARA) of the NUP/UPNA-Public University of Navarre and received an award from the European Association of Fuzzy Logic and Soft Computing (EUSFLAT) during its biennial meeting (EUSFLAT 2013), held in Milan (Italy), in September 2013.

The research has centered on the study of brain images obtained utilizing magnetic resonance imaging (MRI). Specifically, they have developed an algorithm to improve the process to segment the images. “By means of segmentation,” explained Dr. Aránzazu Jurío, one of the researchers of the study. “Each of the objects that make up the image is separated. Each pixel is analyzed so that all the ones sharing the same features are considered to form part of the same object.”

In the instance of medical images, this process is crucial for delimiting tumors where, if one is thinking about areas like those in the brain, 3 mm of difference can mean the difference between a cure and affecting areas controlling speech or vision.

To understand how the algorithm developed by the researchers works, Prof. Humberto Bustince, another investigator on the project, described the following parallel: “Imagine we have the image of a brain by means of magnetic resonance and seven doctors who have to decide how to delimit the tumor. From experience, we know that each one of them will separate the tumor differently. Now, with the proposed method, they will automatically be presented with a set of options, which, in any case, are always going to improve the choice that the worst of the seven may make. We’ve succeeding in improving on the worst: we’ve managed to ensure that even though they may go for the worst option, they will be wrong to the least extent possible.”

Another of the factors that specialists in artificial intelligence have to deal with is that of time variations, because medical images differ in time, and on occasions within a short space of time. “All these algorithms allow application in real time, because what may be useful for an image at a given moment may not be useful after some time,” explained Prof. Bustince.

In this respect, Dr. Jurío stressed that, “the algorithm we have developed produces a kind of consensus among the various functions that seek to obtain the best solution. We could say that it takes the process of choosing away from the expert because it is the algorithm that automatically selects the function.”

“The problem,” said coauthor of the study, Dr. Daniel Paternain, “is that for a specific image there are a number of functions that the expert may use, but if he or she gets the function wrong when doing the segmenting, the result could be devastating. What we tried to solve is this: if he/she gets the function wrong, the result may not be devastating.”

Related Links:
Artificial Intelligence and Approximate Reasoning Group
NUP/UPNA-Public University of Navarre