Preface

Remarkable advances in medical diagnostic imaging have been made during the past few decades. The development of new imaging techniques and continuous improvements in the display of digital images have opened new horizons in the study of brain anatomy and pathology. The field of brain imaging has now become a fastmoving, demanding, and exciting multidisciplinary activity. I hope that this textbook will be useful to students and clinicians in the field of neuroscience, in understanding the fundamentals of advances in brain imaging.

We couldn't have produced the book in less than a year without the excellent team of authors. I am deeply indebted to all the authors. Their contribution and sacrifice made this a worthwhile and proud endeavour.

Special thanks to Ms. Romana Vukelic, publishing process manager for this project, for her tireless and unwavering support of the project.

We are greatly indebted to the production staff of InTech for their patience, valuable guidance and extreme professionalism.

I would also like to thank my family, friends and colleagues for their understanding and encouragement throughout the project.

> **Dr. Vikas Chaudhary**  Department of Radiodiagnosis, Employees' State Insurance Corporation (ESIC) Model Hospital, India

**1** 

*Taiwan, R.O.C.* 

**Automatic Vector Seeded** 

**Classification in Brain MRI** 

Chuin-Mu Wang and Ruey-Maw Chen *National Chin-Yi University of Technology,* 

**Region Growing for Parenchyma** 

Nuclear magnetic resonance (NMR) can be used to measure the nuclear spin density, the interactions of the nuclei with their surrounding molecular environment and those between close nuclei, respectively. It produces a sequence of multiple spectral images of tissues with a variety of contrasts using several magnetic resonance parameters. When tissues are classified by means of MRI, the images are multi-spectral. Therefore, if only a single image with a certain spectrum is processed, the goal of tissue classification will not be achieved because the single image can't provide adequate information. Consequently, it is necessary to integrate the information of all the spectral images to classify tissues. Multi-spectral image processing techniques [1-3] are hence employed to collect spectral information for classification and of clinically critical values. In this paper, a new classification approach was proposed, it is called unsupervised Vector Seeded Region Growing (UVSRG). The UVSRG mainly select seed pixel vectors by means of standard deviation and relative Euclidean distance. Through the UVSRG processing, the data dimensionality of MRI can be decreased and the desired target of interest can be classified which the brain tissue and brain tumor segmentation. A series of experiments are conducted and compared to the commonly used c-means method for performance evaluation. The results show that the proposed approach is a promising and effective

Nuclear magnetic resonance (NMR) has recently developed as a versatile technique in many fields such as chemistry, physics, engineering because its signals provide rich information about material structures that involve the nature of a population of atoms, the structure of their environment, and the way in which the atoms interact with environment1. When NMR is applied to human anatomy, NMR signals can be used to measure the nuclear spin density, the interactions of the nuclei with their surrounding molecular environment and those between close nuclei, respectively. It produces a sequence of multiple spectral images of tissues with a variety of contrasts using three magnetic resonance parameters, spin-lattice (T1), spin-spin (T2) and dual echo-echo proton density (PD). By appropriately choosing pulse sequence parameters, echo time (TE) and repetition time (TR) a sequence of images of

**1. Introduction** 

technique for MR image classification.
