x 1. INTRODUCTION 1 1.1 Medical Imaging 1 1.2 Medical Image Segmentation 4 1.3 Digital Image Processing 7 1.4 Motivation 9 1.5 Thesis Organization 10 2. LITERATURE REVIEW 12 2.1 Segmentation 12 2.1.1 Classical Approaches 13 2.1.2 Advanced Approaches 14 2.1.2.1 Statistical and Probabilistic Approaches 14 2.1.2.2 Deformable Models 15 2.1.2.3 Artificial Neural Networks (ANNs) 16 2.1.2...

In this research, a hybrid medical image segmentation in both spatial and frequency domain is proposed. It is aimed to detect tumor and find the accuracy detection in both spatial and frequency domain. An important algorithm is added to our research which is finding the biggest contour algorithm. This algorithm used to detect tumor region in digital image that differ in properties and provide c...

We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images...

The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmenta...

Deformable image registration is a key enabling technology for advanced treatment of lung cancer patients, as it can facilitate motion estimation, structure segmentation, as well as dose tracking and accumulation. In this work, we developed a hybrid feature-constrained deformable registration method and applied it to tackle the EMPIRE10 (Evaluation of Methods for Pulmonary Image Registration 20...

An evaluation of two traditional segmentation algorithms of Morphological Reconstruction and the Fast Marching method along with a novel hybrid segmentation approach is presented. After introducing the Fast Marching and the Morphological Reconstruction segmentation, we propose a novel hybrid segmentation approach in multi-stage, which is derived from both an improved Fast Marching method and th...

The fuzzy c-means (FCM) has been a well-known algorithm in machine learning/data mining area as a clustering algorithm. It can also be used for image segmentation, but the algorithm is not robust to noise. The possibilistic c-means (PCM) algorithm was proposed to overcome such a problem. However, the performance of PCM is too sensitive to the initialization of cluster centers, and often deterio...

this paper introduces a novel methodology for the segmentation of brain ms lesions in mri volumes using a new clustering algorithm named scpfcm.  scpfcm uses membership, typicality and spatial information to cluster each voxel. the proposed method relies on an initial segmentation of ms lesions in t1-w and t2-w images by applying scpfcm algorithm, and the t1 image is then used as a mask and is ...

Unsupervised image segmentation algorithms have matured to the point where they generate reasonable segmentations, and thus can begin to be incorporated into larger systems. A system designer now has an array of available algorithm choices, however, few objective numerical evaluations exist of these segmentation algorithms. As a first step towards filling this gap, this paper presents an evalua...