**Meet the editor**

Dr. Pei-Gee Peter Ho received his BSEE from National Cheng Kung University, Taiwan in 1976 and MSEE from University of Massachusetts at Dartmouth in 1981. During the last 20 plus years he has worked in various computer engineering companies. He received his Ph.D. degree in EE from UMass Dartmouth in January 2008. He is now working in the DSP algorithm group of Naval

Undersea Warfare Center at Newport, Rhode Island and has published four books and a few journal papers in the area of image processing and acoustic digital communications.

Contents

**Preface VII**

Larhmam

**Section 1 Advances in Image Segmentation 1**

Chapter 1 **Template Matching Approaches Applied to Vertebra Detection 3**

Ronny Vallejos and Silvia Ojeda

**Color Image Segmentation 81** P. K. Nanda and Sucheta Panda

**the Mean Shift 49**

Juan H. Sossa

Mohammed Benjelloun, Saïd Mahmoudi and Mohamed Amine

Roberto Rodríguez Morales, Didier Domínguez, Esley Torres and

Luciano Cássio Lulio, Mário Luiz Tronco, Arthur José Vieira Porto, Carlos Roberto Valêncio and Rogéria Cristiane Gratão de Souza

Chapter 4 **Constrained Compound MRF Model with Bi-Level Line Field for**

Chapter 5 **Cognitive and Statistical Pattern Recognition Applied in Color and Texture Segmentation for Natural Scenes 103**

Chapter 2 **Image Segmentation and Time Series Clustering Based on Spatial and Temporal ARMA Processes 25**

Chapter 3 **Image Segmentation Through an Iterative Algorithm of**

## Contents

**Preface XI**

**Section 1 Advances in Image Segmentation 1**


**and Texture Segmentation for Natural Scenes 103** Luciano Cássio Lulio, Mário Luiz Tronco, Arthur José Vieira Porto, Carlos Roberto Valêncio and Rogéria Cristiane Gratão de Souza

Preface

Generally speaking, image processing applications for computer vision consist of enhancement, reconstruction, segmentation, recognition and communications. In the last

The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. In the medical world, it is interested to detect and extract vertebra locations from X-ray images. The generalized Hough Transform to detect vertebra positions and orientations is proposed. The spatial autoregressive moving average (ARMA) processes have been extensively used in several applications in image and signal processing. In particular, these models have been used for image segmentation. The Mean shift (MSH) method is a robust technique which has been applied in many computer vision tasks. The MSH procedure moves to a kernel-weighted average of the observations within a smoothing window. This computation is repeated until convergence is obtained at a local density mode. The density modes can be located without explicitly estimating. The Constrained Markov Random Field (MRF) model has the unifying property of modeling scene as well as texture images. The scheme is specifically meant to preserve weak edges besides the well defined strong edges. By Statistical Pattern Recognition approach, the cognitive and statistical classifiers were implemented in order to verify the estimated and

Following our previous popular artificial intelligent book "Image Segmentation", ISBN 978-953-307-228-9, published on April 19, 2011, this new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years. The book presented chapters that highlight frontier works in image information processing. I am pleased to have leaders in the field to prepare and contribute their most current research and development work. Although no attempt is made to cover every topic, these entire five special chapters shall give readers a

**Pei-Gee Peter Ho**

DSP Algorithm and Software Design Group,

Naval Undersea Warfare Center Newport, Rhode Island, USA

few years, image segmentation played an important role in image analysis.

chosen regions on unstructured environments images.

deep insight. All topics listed are equal important and significant.

## Preface

Generally speaking, image processing applications for computer vision consist of enhancement, reconstruction, segmentation, recognition and communications. In the last few years, image segmentation played an important role in image analysis.

The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. In the medical world, it is interested to detect and extract vertebra locations from X-ray images. The generalized Hough Transform to detect vertebra positions and orientations is proposed. The spatial autoregressive moving average (ARMA) processes have been extensively used in several applications in image and signal processing. In particular, these models have been used for image segmentation. The Mean shift (MSH) method is a robust technique which has been applied in many computer vision tasks. The MSH procedure moves to a kernel-weighted average of the observations within a smoothing window. This computation is repeated until convergence is obtained at a local density mode. The density modes can be located without explicitly estimating. The Constrained Markov Random Field (MRF) model has the unifying property of modeling scene as well as texture images. The scheme is specifically meant to preserve weak edges besides the well defined strong edges. By Statistical Pattern Recognition approach, the cognitive and statistical classifiers were implemented in order to verify the estimated and chosen regions on unstructured environments images.

Following our previous popular artificial intelligent book "Image Segmentation", ISBN 978-953-307-228-9, published on April 19, 2011, this new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years. The book presented chapters that highlight frontier works in image information processing. I am pleased to have leaders in the field to prepare and contribute their most current research and development work. Although no attempt is made to cover every topic, these entire five special chapters shall give readers a deep insight. All topics listed are equal important and significant.

> **Pei-Gee Peter Ho** DSP Algorithm and Software Design Group, Naval Undersea Warfare Center Newport, Rhode Island, USA

**Chapter 1**

**Template Matching Approaches Applied to Vertebra**

In the medical world, the problems of back and spine are usually inseparable. They can take various forms ranging from the low back pain to scoliosis and osteoporosis. Medical Imag‐ ing provides very useful information about the patient's condition, and the adopted treat‐ ment depends on the symptoms described and the interpretation of this information. This information is generally analyzed visually and subjectively by a human expert. In this diffi‐ cult task, medical images processing presents an effective aid able to help medical staff. This

We are particularly interested to detect and extract vertebra locations from X-ray images. Some works related to this field can be found in the literature. Actually, these contributions are mainly interested in only 2 medical imagery modalities: Computed Tomography (CT) and Magnetic Resonance (MR). A few works are dedicated to the conventional X-Ray radi‐ ography. However, this modality is the cheapest and fastest one to obtain spine images. In addition, from the point of view of the patient, this procedure has the advantage to be more safe and non-invasive. For these reasons, this review is widely used and remains essential treatments and/or urgent diagnosis. Despite these valuable benefits, the interpretation of im‐ ages of this type remains a difficult task now. Their nature is the main cause. Indeed, in practice, these images are characterized by a low contrast and it is not uncommon that some parts of the image are partially hidden by other organs of the human body. As a result, the

In the context of cervical spinal column analysis, the vertebra edges detection task is very useful for further processing, like angular measures (between two consecutive vertebrae or

> © 2012 Benjelloun et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Benjelloun et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

is nowhere clearer than in diagnostics and therapy in the medical world.

vertebra edge is not always obvious to see or detect.

Mohammed Benjelloun, Saïd Mahmoudi and

Additional information is available at the end of the chapter

Mohamed Amine Larhmam

http://dx.doi.org/10.5772/50476

**1. Introduction**

**Detection**
