CIT 2016 - Special Issue "Computational Intelligence Technique in Medical Image Analysis"

Computational Intelligence (CI) is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches are ineffective or infeasible. The techniques of computational intelligence have been successfully developed in areas such as artificial neural networks, fuzzy systems, and evolutionary algorithms.
On the other hand, medical image analysis has become an essential component in many fields of bio-medical research and clinical practice. Radiologists identify and quantify tumors from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, physicians detect breast cancer from mammograms, and neuroscientists detect regional metabolic brain activity from Positron Emission Tomography (PET) and functional MRI scans. In a word, medical image analysis offers useful information on patients’ medical conditions and clues to the causes of their symptoms and diseases.
However, medical images provide a mass of information that physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians’ decision-making. In last decade, computational intelligence has been used in medical imaging, such as in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy.
This Special Issue aims to provide a comprehensive overview of the state-of-the-art computational intelligence research and technologies in medical image analysis. Scholars are invited to contribute original studies that have reached the practical level, and those technologies that are rapidly becoming available in clinical practices in hospitals, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.
Potential topics may include, but are not limited to:
The use of CI techniques such as:
Pattern Recognition Methods, such as artificial neural networks, support vector machine, deep learning
Swarm intelligence, such as ant colony optimization, particle swarm optimization, artificial bee colony, glowworm swarm optimization, multi-swarm optimization
Fuzzy logics, such as fuzzy rough set, fuzzy control, propositional and predicate fuzzy logics
Chaos theory, such as chaotic maps, logistic map, Horseshoe map
Other formalisms, such as expert system, concept mining, data mining, mathematical analysis, intelligence agent, fractional calculus
for the following biomedical imaging technologies
X-ray radiography
Endoscopy
Ultrasound, Ultrasonography
CT
PET, SPECT
Magnetic Resonance Imaging
EEG, MEG, EKG