Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images
Nosratallah Forghani, Mohamad Forouzanfar, Armin Eftekhari, Shahab Mohammad-Moradi, Mohammad Teshnehlab · InTech eBooks · 2009
IntroductionMedical imaging refers to the techniques and processes used to obtain images of the human body for clinical purposes or medical science.Common medical imaging modalities include ultrasound (US), computerized tomography (CT), and magnetic resonance imaging (MRI).Medical imaging analysis is usually applied in one of two capacities: i) to gain scientific knowledge of diseases and their effect on anatomical structure in vivo, and ii) as a component for diagnostics and treatment planning (Kannan, 2008).Medical US uses high frequency broadband sound waves that are reflected by tissue to varying degrees to produce 2D or 3D images.This is often used to visualize the fetus in pregnant women.Other important uses include imaging the abdominal organs, heart, male genitalia, and the veins of the leg.US has several advantages which make it ideal in numerous situations.It studies the function of moving structures in real-time, emits no ionizing radiation, and contains speckle that can be used in elastography.It is very safe to use and does not appear to cause any adverse effects.It is also relatively cheap and quick to perform.US scanners can be taken to critically ill patients in intensive care units, avoiding the danger caused while moving the patient to the radiology department.The real time moving image obtained can be used to guide drainage and biopsy procedures.Doppler capabilities on modern scanners allow the blood flow in arteries and veins to be assessed.However, US images provides less anatomical detail than CT and MRI (Macovski, 1983).CT is a medical imaging method employing tomography (Slone et al., 1999).Digital geometry processing is used to generate a three-dimensional image of the inside of an object from a large series of two-dimensional X-ray images taken around a single axis of rotation.CT produces a volume of data which can be manipulated, through a process known as windowing, in order to demonstrate various structures based on their ability to block the Xray beam.Although historically the images generated were in the axial or transverse plane (orthogonal to the long axis of the body), modern scanners allow this volume of data to be reformatted in various planes or even as volumetric (3D) representations of structures.CT was the first imaging modality to provide in vivo evidence of gross brain morphological abnormalities in schizophrenia, with many CT reports of increase in cerebrospinal fluid (CSF)-filled spaces, both centrally (ventricles), and peripherally (sulci) in a variety of psychiatric patients. www.intechopen.com Particle Swarm Optimization 204MRI is a technique that uses a magnetic field and radio waves to create cross-sectional images of organs, soft tissues, bone and virtually all other internal body structures.MRI is based on the phenomenon of nuclear magnetic resonance (NMR).Nuclei with an odd number of nucleons, exposed to a uniform static magnetic field, can be excited with a radio frequency (RF) pulse with the proper frequency and energy.After the excitation pulse, NMR signal can be recorded.The return to equilibrium is characterized by relaxation times T1 and T2, which depend on the nuclei imaged and on the molecular environment.Mainly hydrogen nuclei (proton) are imaged in clinical applications of MRI, because they are most NMR-sensitive nuclei (Haacke et al., 1999).MRI possesses good contrast resolution for different tissues and has advantages over computerized tomography (CT) for brain studies due to its superior contrast properties.In this context, brain MRI segmentation is becoming an increasingly important image processing step in many applications including: i) automatic or semiautomatic delineation of areas to be treated prior to radiosurgery, ii) delineation of tumours before and after surgical or radiosurgical intervention for response assessment, and iii) tissue classification (Bondareff et al., 1990).Several techniques have been developed for brain MR image segmentation, most notably thresholding (Suzuki & Toriwaki, 1991), edge detection (Canny, 1986), region growing (Pohle &Toennies, 2001), and clustering (Dubes &Jain, 1988).Thresholding is the simplest segmentation method, where the classification of each pixel depends on its own information such as intensity and colour.Thresholding methods are efficient when the histograms of objects and background are clearly separated.Since the distribution of tissue intensities in b r a i n M R i m a g e s i s o f t e n v e r y c o m p l e x , these methods fail to achieve acceptable segmentation results.Edge-based segmentation methods are based on detection of boundaries in the image.These techniques suffer from incorrect detection of boundaries due to noise, over-and under-segmentation, and variability in threshold selection in the edge image.These drawbacks of early image segmentation methods, has led to region growing algorithms.Region growing extends thresholding by combining it with connectivity conditions or region homogeneity criteria.However, only well defined regions can be robustly identified by region growing algorithms (Clarke et al., 1995).Since the above mentioned methods are generally limited to relatively simple structures, clustering methods are utilized for complex pathology.Clustering is a method of grouping data with similar characteristics into larger units of analysis.Expectation-maximization (EM) (Wells et al., 1996), hard c-means (HCM) and its fuzzy equivalent, fuzzy c-means (FCM) algorithms (Li et al., 1993) are the typical methods of clustering.A common disadvantage of EM algorithms is that the intensity distribution of brain images is modeled as a normal distribution, which is untrue, especially for noisy images.Since Zadeh (1965) first introduced fuzzy set theory which gave rise to the concept of partial membership, fuzziness has received increasing attention.Fuzzy clustering algorithms have been widely studied and applied in various areas.Among fuzzy clustering techniques, FCM is the best known and most powerful method used in image segmentation.Unfortunately, the greatest shortcoming of FCM is its over-sensitivity to noise, which is also a drawback of many other intensity-based segmentation methods.Since medical images contain significant amount of noise caused by operator, equipment, and the environment, there is an essential need for development of less noise-sensitive algorithms.Many extensions of the FCM algorithm have been reported in the literature to overcome the effects of noise, such as noisy clustering (NC) (Dave, 1991), possibilistic c-means (PCM) www.intechopen.com