Segmentation of Brain Magnetic Resonance Images (MRIs): A Review

Abstract MR imaging modality has assumed an important position in studying the characteristics of soft tissues. Generally, images acquired by using this modality are found to be affected by noise, partial volume effect (PVE) and intensity nonuniformity (INU). The presence of these factors degrades the quality of the image. As a result of which, it becomes hard to precisely distinguish between different neighboring regions constituting an image. To address this problem, various methods have been proposed. To study the nature of various proposed state-of-the-art medical image segmentation methods, a review was carried out. This paper presents a brief summary of this review and attempts to analyze the strength and weaknesses of the proposed methods. The review concludes that unfortunately, none of the proposed methods has been able to independently address the problem of precise segmentation in its entirety. The paper strongly favors the use of some module for restoring pixel intensity value along with a segmentation method to produce efficient results

Knee cartilage segmentation using multi purpose interactive approach

Interactive model incorporates expert interpretation and automated segmentation. However, cartilage has complicated structure, indistinctive tissue contrast in magnetic resonance image of knee hardens image review and existing interactive methods are sensitive to various technical problems such as bi-label segmentation problem, shortcut problem and sensitive to image noise. Moreover, redundancy issue caused by non-cartilage labelling has never been tackled. Therefore, Bi-Bezier Curve Contrast Enhancement is developed to improve visual quality of magnetic resonance image by considering brightness preservation and contrast enhancement control. Then, Multipurpose Interactive Tool is developed to handle users’ interaction through Label Insertion Point approach. Approximate NonCartilage Labelling system is developed to generate computerized non-cartilage label, while preserves cartilage for expert labelling. Both computerized and interactive labels initialize Random Walks based segmentation model. To evaluate contrast enhancement techniques, Measure of Enhancement (EME), Absolute Mean Brightness Error (AMBE) and Feature Similarity Index (FSIM) are used. The results suggest that Bi-Bezier Curve Contrast Enhancement outperforms existing methods in terms of contrast enhancement control (EME = 41.44±1.06), brightness distortion (AMBE = 14.02±1.29) and image quality (FSIM = 0.92±0.02). Besides, implementation of Approximate Non-Cartilage Labelling model has demonstrated significant efficiency improvement in segmenting normal cartilage (61s±8s, P = 3.52 x 10-5) and diseased cartilage (56s±16s, P = 1.4 x 10-4). Finally, the proposed labelling model has high Dice values (Normal: 0.94±0.022, P = 1.03 x 10-9; Abnormal: 0.92±0.051, P = 4.94 x 10-6) and is found to be beneficial to interactive model (+0.12)

Model based three dimensional medical image segmentation

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 115-123).by Tina Kapur.Ph.D

Statistical models in medical image analysis

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 149-156).Computational tools for medical image analysis help clinicians diagnose, treat, monitor changes, and plan and execute procedures more safely and effectively. Two fundamental problems in analyzing medical imagery are registration, which brings two or more datasets into correspondence, and segmentation, which localizes the anatomical structures in an image. The noise and artifacts present in the scans, combined with the complexity and variability of patient anatomy, limit the effectiveness of simple image processing routines. Statistical models provide application-specific context to the problem by incorporating information derived from a training set consisting of instances of the problem along with the solution. In this thesis, we explore the benefits of statistical models for medical image registration and segmentation. We present a technique for computing the rigid registration of pairs of medical images of the same patient. The method models the expected joint intensity distribution of two images when correctly aligned. The registration of a novel set of images is performed by maximizing the log likelihood of the transformation, given the joint intensity model. Results aligning SPGR and dual-echo magnetic resonance scans demonstrate sub-voxel accuracy and large region of convergence. A novel segmentation method is presented that incorporates prior statistical models of intensity, local curvature, and global shape to direct the segmentation toward a likely outcome. Existing segmentation algorithms generally fit into one of the following three categories: boundary localization, voxel classification, and atlas matching, each with different strengths and weaknesses. Our algorithm unifies these approaches. A higher dimensional surface is evolved based on local and global priors such that the zero level set converges on the object boundary. Results segmenting images of the corpus callosum, knee, and spine illustrate the strength and diversity of this approach.by Michael Emmanuel Leventon.Ph.D

Adaptive Template Moderated Brain Tumor Segmentation in MRI

. This paper describes a new method for the automated segmentation of MRI images of brain tumors. The algorithm is an iterative, hierarchical approach that integrates a statistical classification scheme and anatomical knowledge from an aligned digital atlas. For validation, the method was applied to 10 tumor cases in different locations in the brain including meningiomas and astrocytomas (grade 1--3). The brain and tumor segmentation results were compared to manual segmentations carried out by 4 independent medical experts. It is demonstrated that the algorithm produces results of comparable accuracy to those of the manual segmentations in a shorter time. Keywords: Image Registration, Magnetic Resonance, Brain Tumor, Template based Segmentation 1 Introduction Many applications of computer assisted neuro-surgery and-radiology rely on previously segmented medical images. However, this task often requires labor intensive and time consuming manual interaction. The goal was to ..

Adaptive template moderated brain tumor segmentation in MRI. Bildverarbeitung in der Medizin

Abstract. This paper describes a new method for the automated segmentation of MRI images of brain tumors. The algorithm is an iterative, hierarchical approach that integrates a statistical classification scheme and anatomical knowledge from an aligned digital atlas. For validation, the method was applied to 10 tumor cases in different locations in the brain including meningiomas and astrocytomas (grade 1-3). The brain and tumor segmentation results were compared to manual segmentations carried out by 4 independent medical experts. It is demonstrated that the algorithm produces results of comparable accuracy to those of the manual segmentations in a shorter time