Segmentation of 3D medical images based on region growing method

Táto bakalárska práca sa zaoberá segmentáciou medicínskych objemových dát pomocou metódy narastania oblastí. Cieľom je popísať hlavné metódy 3D segmentácie obrazových dát a zamerať sa najmä na metódu narastania oblastí. Vstupnými dátami sú snímky rezov mozgu z magnetickej rezonancie, ktoré je možné pomocou navrhnutého prehliadača zobrazovať v troch základných rovinách. Prehliadač je realizovaný v programovom prostredí Matlab. Segmentácia obrazových dát je realizovaná metódou semienkového narastania oblastí.This bachalor thesis deals with a region growing approach for segmentation of volumetric medical images. The aim is to present basic methods of segmentation of image data and to focus in particular on the approach of region growing. The input data are brain slices of magnetic resonance imaging which can be visualized using the browser into the three basic planes. The viewer is implemented in MATLAB programming environment. Image segmentation is realized by seeded region growing.

Segmenting CT images of bronchogenic carcinoma with bone metastases using PET intensity markers approach

Background: The evolution of medical imaging plays a vital role in the management of patients with cancer. In oncology, the impact of PET/CT imaging has been contributing widely to the patient treatment by its large advantages over anatomical imaging from screening to staging. PET images provide the functional activity inside the body while CT images demonstrate the anatomical information. Hence, the existence of cancer cells can be recognized in PET image but since the structural location and position cannot be defined on PET images, we need to retrieve the information from CT images. Methods: In this study, we highlight the localization of bronchogenic carcinoma by using high activity points on PET image as references to extract regions of interest on CT image. Once PET and CT images have been registered using cross correlation, coordinates of the candidate points from PET are fed into seeded region growing algorithm to define the boundary of lesion on CT. The region growing process continues until a significant change in bilinear pixel values is reached. Results: The method has been tested over eleven images of a patient having bronchogenic carcinoma with bone metastases. The results show that the mean standard error for over segmented pixels is 33% while for the under segmented pixels is 3.4%. Conclusions: Although very simple in implementation, region growing can result in good precision ROIs. The region growing method highly depends on where the growing process starts. Here, by using the data acquired from other modality, we tried to guide the segmentation process to achieve better segmentation results

A Method of Segmentation for Hyper spectral & Medical Images Based on Color Image Segmentation

The paper propose an original and simple segmentation strategy based on the EM approach for hyper spectral images . In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified. The spatial constraint allows taking into account the inherent spatial relationships of any image and its colours. This approach provides effective PSNR for the segmented image. These results omit the better performance athe segmented images are compared with Watershed & Region Growing Algorithm. This approach provides the effective segmentation for the Spectral Images & Medical Images. With proposed approach it can be fascinated that the data obtained from the segmentation can provide accurate information from the huge image

An automatic and robust algorithm for segmentation of three-dimensional medical images

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Segmentation is a crucial precursor to most medical image analysis applications. This paper presents a new three-dimensional adaptive region growing algorithm for the automatic segmentation of three-dimensional images. The principle of our algorithm is to obtain a satisfactory segment result by self-tuning the homogeneity constraint step by step, which effectively resolves the dilemma of threshold auto-selection. Novel homogeneity and leakage detection criteria are designed to improve accuracy and robustness. Cavities auto-filling algorithm is also proposed to eliminate the interior cavities. Our algorithm was tested by segmenting lungs from 3D throat CT images and compared with manual segmentation and traditional 3D region growing. Results demonstrate that our algorithm greatly outperforms traditional 3D region growing method and its segment result is close to that of manual segmentation.Haibo Zhang, Hong Shen, Huichuan Dua

Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation

Segmentation, a new method, for color, gray-scale MR medical images, and aerial images, is proposed. The method is based on gray-scale morphology. Edge detection algorithm includes function edge and marker-controlled watershed segmentation. It features the simple algorithm implemented in MATLAB. The watershed segmentation has been proved to be a powerful and fast technique for both contour detection and region-based segmentation. In principle, watershed segmentation depends on ridges to perform a proper segmentation, a property that is often fulfilled in contour detection where the boundaries of the objects are expressed as ridges. For region-based segmentation, it is possible to convert the edges of the objects into ridges by calculating an edge map of the image. Watershed is normally implemented by region growing, based on a set of markers to avoid oversegmentation

Automated Segmentation of Retinal Vasculature

Image processing, analysis and computer vision techniques are increasing in all fields of medical science, and are especially applicable to modern ophthalmology. Automated image segmentation processing has the prospective for early detection of many diseases like the diabetes, by detecting changes in blood vessel in the retina images . The focus of this poster is on the automated segmentation of vessels in color images of the retina by describes the development of segmentation methodology in the processing of retinal blood vessel images using the region growing method and the Powerlaw transformation . The retina is the only location where blood vessels can be directly visualized non-invasively in vivo. Inspection of the retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease, and stroke. In the same time with suitable feature extraction and automated classification methods, this segmentation method could form the basis of a quick and accurate test for the retina image, which would have many benefits for improved the access to screening people for risk or presence of diseases

Automatically Extract the Left and Right Ventricular Myocardium from CT Images by Using Region Based Segmentation

ABSTRACT: Seeded Region Growing is the more gorgeous method in medical image segmentation by using high level information of images for selecting seeds for segmentation. This SRG algorithm that provides the most efficient for fixing the labels for pixels and also for segmentation. This paper represents the automatic system for segmenting the left and right ventricular myocardium CT images by using Region growing method. This algorithm follows the focus on fixing the labels for region growing segmentation. This algorithm that gives the most accurate segmentation of Myocardium from CT images the delineation of the myocardial wall which is a exacting task due to large differences in myocardial shapes and quality of an image. In this paper, we describe an automatic method for extracting the myocardium from the left and right ventricles from CT images. In the method, the left and right ventricles are detected, by first identifying the endocardium and epicardium and then segmenting the myocardium. After that, a seed regiongrowing method is applied to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result, which gives the high accuracy

A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i4.625

Empirical evaluation of segmentation algorithms for lung modelling

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Lung modelling has emerged as a useful method for diagnosing lung diseases. Image segmentation is an important part of lung modelling systems. The ill-defined nature of image segmentation makes automated lung modelling difficult. Also, low resolution of lung images further increases the difficulty of the lung image segmentation. It is therefore important to identify a suitable segmentation algorithm that can enhance lung modelling accuracies. This paper investigates six image segmentation algorithms, used in medical imaging, and also their application to lung modelling. The algorithms are: normalised cuts, graph, region growing, watershed, Markov random field, and mean shift. The performance of the six segmentation algorithms is determined through a set of experiments on realistic 2D CT lung images. An experimental procedure is devised to measure the performance of the tested algorithms. The measured segmentation accuracies as well as execution times of the six algorithms are then compared and discussed.S.L.A. Lee, A.Z. Kouzani, and E.J. H

A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction