Research And Application Of Medical Image Segmentation Methods

With the development of computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and other imaging technologies, diagnostic imaging is a critical component in clinical medicine today. These modalities have greatly improved knowledge of normal and diseased anatomy and functional metabolism of the anatomy for medical clinicians and provide a valuable tool for clinical diagnosis, making aggressive treatment planning, and numerous biomedical imaging researches. With the increasing size and number of medical image data, more and more scientific researchers and clinicians make contribute to facilitating the data processing and analyzing by the usage of computers. Medical image segmentation, the delineation of anatomical structures and other regions of interest, is the main key for patterns analysis and clustering as well as plays an important role in3D visualization, quantitive analysis on tissue characterization,3D localization and making surgery planning.Medical imaging segmentation consists of three categories:manual segmentation, semi-automated segmentation and automated segmentation. The manual segmentation is performed by the clinical experts using the designed tool for delineating the boundaries of the object, and this method has good accuracy but is prohibitively a labor intensive, tedious and time-consuming task, not meets the real-time requirement in clinical practice. Semi-automated segmentation algorithms make full use of the computers to extract the boundaries of the object by taking the knowledge of the experienced experts into account, which limits the application in clinical practice. Researchers and clinicians pay attention on developing automated segmentation methods, which facilitate to process the image data by taking advantage of computers and detect the boundaries of the object automatically. They could avoid the subjectivity from the worldwide operators and improve the efficiency of data processing as well as good reproducibility. The automated or semi-automated algorithms are highly desired and developed to deal with the various objects and solve the specific problem.We have developed the automated segmentation methods of the MR images in this paper. The main contributions of this paper are listed as follows.First, we present a novel method for the automated segmentation of the vertebral bodies from2D sagittal MR images of the spine based on local spatial information and Gaussian weighted chi-square distance.Due to the aging of the society and the increasing of work pressure, a large population has the spondylopathy, such as pleurapophysis, slipped disc and spinal stenosis, which influence lives of people in various trades. Spinal anatomica structure and circum-structure of the vertebrae are very complicated, thus the clinician must has good localization and good knowledge of the vertebrae. The recognition of the vertebral body is the main key of improving efficiency, accuracy and security of the operation in clinical practice.Physicians often make their decisions in diagnosis and treatment of spine with the help of CT and MR images. CT images show reasonably high resolution and give good visualization of the bone, and the vertebral body segmentation is very simple using the thresholding method. MR makes full use of a strong magnetic field (BO field) and radiofrequency pulses for generating images. If the image intensity is determined by the T1relaxation, it is T1-weighted imaging. If the image intensity is determined by the T1relaxation, it is T2-weighted imaging. Tl-weighted scans work well for differentiating fat from water with water appearing darker and fat brighter. This scans are obtained to observe the anatomical structure of the spine. T2-weighted scans are another basic type. Like the T1-weighted scan, fat is differentiated from water but in this case fat shows darker, and water lighter. In the case of spinal study, the cerebrospinal fluid will be lighter in T2-weighted images. This scans are acquired to examine the pathologic change of the spine. The segmentation of vertebral bodies in MR images is much challenging and complex due to the relatively variations in soft tissue contrast and artifacts like radio-frequency inhomogeneity. There is little work on the vertebral body extraction from the sagittal MR spinal images.Gamio et al. applied graph-cut to segment MR T1-weighted sagittal images of the spine. Graph-cut is an unsupervised method and does not require initialization. Using methods based on graph cut, an image is usually segmented into several distinct regions rather than the target and the background, and the pixels have high similarity within each region. A weighted graph is constructed, where nodes of the graph correspond to image pixels, and the weight of the edge reflects the similarity between two joined nodes. Solving the eigenvectors and eigenvalues of the affinity matrix performs the image segmentation. This method defined a partitioning criterion that maximizes the total similarity within groups and minimizes the total similarity between different groups. Gamio et al. segmented the vertebral body using windowed histograms of intensity as the most promising features. Due to the usage of the simple statistical characteristics of local histogram, Gamio algorithm is not a good choice for segmenting the images with same statistical characteristics of local histogram and low-contrast objects.We present develop a new approach to automatically segment vertebral bodies from spinal MR T1-weighted and T2-weighted sagittal images. Our methodology is novel in the following ways. To build a new affinity matrix for advanced image segmentation, we first use a cut-off window (5×5) around each pixel and stack the gray values inside the window into a vector, which local intensity is introduced to depict the image exactly and help to distinguish different tissues and suppress the effect of noise. Second, considering the contribution of the nearby pixels to the centered pixel, we adopt the Gaussian kernel function to incorporate local spatial information, thus allowing the suppression of noise and improving the accuracy of the segmentation. Third, an adaptive local scaling parameter is used to refine the segmentation rather than a fixed scaling parameter to avoid the manually tuned parameter. Finally, the built affinity is introduced into the segmentation process by using a graph-based method to achieve the complete target. Sagittal MR images of the spine were performed with a3.0T scanner at Nanfang Hospital, Guangzhou, China. The final data set comprised of100images (34healthy and66unhealthy). Extensively experiments show that the present method can segment the vertebral bodies smoothly and clearly, and it has stronger anti-noise property and higher segmentation precision than the conventional methods. The robust and accurate result of segmentation should serve image registration and the analysis of spinal deformities. It can also be used in organ location and image-guided vertebra operation, with presumed significant clinical impact. It is a general method for segmenting object that can develop to segment other tissues and organs.Second, we develop a Gaussian Chan-Vese(CV) model based on entropy and local neighborhood information for the tumor extraction from brain MR image.Tumor is one of the most common brain diseases, so its diagnosis and treatment is valuable for more than4000000persons per year in the world. The brain tumors have a great diversity in shape and appearance with intensities among individuals. It is still a challenge for automatically extracting the brain tumor, which helps contribute to3D visualization, computer aided pathologies diagnosis and surgical guidance for the clinicians in practice.Active contour model is one of the most popular image segmentation methods. The basic idea of the active contour model is to evolve a closed curve to extract the object. The energy function gets to the minimum and the closed curve gets to the boundary of the region of interest. The conventional CV model assumed the homogeneity of image intensities, and it cannot extract the tumor from the brain MR images with inhomogeneities of image intensities in the region of interest. We develop an adaptive Gaussian Chan-Vese(CV) model based on entropy and local neighborhood information for the tumor extraction from brain MR image. The main contributions are as follows:(1) In the cost function of this model, the interior and exterior energies are weighted by the entropy, which improves the robust of the evolving curve;(2) The local information of the curve is considered rather than global image statistics, which reduces the impact of the heterogeneous grays inside of regions and improves the segmentation results.(3) The Gaussian kernel is utilized to regularize the level set function, which not only keeps the level set function smooth and stable, but also removes the traditional Euclidean length term and re-initialization. The final data set comprised of100barin images, which were obtained using a3.0T scanner at Tianjin Medical Universiy General Hospital, Tianjin, China, for validating the present method. Extensively experiments show that the present method can segment the tumor from the brain MR images in terms of high accuracy.Third, we develop and validate an automated method of interventricular septum segmentation (AISS) from myocardial black-blood images for the T2*measurement in thalassemia patients.The thalassemia is the most common monogenic inheritance disorders in the world. The thalassemia major often requires the chronic blood transfusions, which can prevent bone lesions and has greatly prolonged survival and maintained a reasonable quality of life. However, long-term transfusion therapy can result in progressive accumulation of iron, which can cause damage in many organs, particularly the liver, heart, and endocrine organs, ultimately leading to increased morbidity and mortality. Chelation therapy can remove excessive tissue iron from the body and reduce the risk of organ failure in these patients. Thus accurate and robust measurement of myocardial iron concentration is clinically vital for tailoring appropriate iron-chelating treatment and assessing the prognosis in thalassemia patients.Myocardial iron deposits can be measured by using endomyocardial biopsy, but which is invasive with the risk of complications and uncertainty owing to the inhomogeneous myocardial iron distribution and quantification errors caused by very small biopsy samples. MR has been established as a non-invasive approach for detecting myocardial iron content (MIC) in various patients with iron overload. T2*is a form of T2with higher sensitivity to field changes caused by iron. Tissue iron can be measured indirectly from the effect of particulate iron within myocytes on relaxation times of hydrogen nuclei. The presence of iron causes a local disruption of the magnetic field, thereby shortening the relaxation times and these quantifiable effects can be calibrated for absolute iron concentration.T2*is quicker and easier to measure in the heart than T2, and is more sensitive to iron and less sensitive to cardiac motion. A mid-ventricular short-axis slice was chosen with a region of interest in the septum to give a uniform myocardial segment free of artefact for the measurement of T2*decay. In routine clinical practice, a representative T2*value of a region-of interest is generally reported as a quantitative marker of MIC. T2*measurements are preferably performed in the region of interventricular septum, as this region is least affected by severe susceptibility effects from epicardial fat, liver and lung tissues. The normal range for T2*(derived from a group of normal individuals with no history of transfusion or cardiac disease) is52±16ms, with a median normal value of40ms. The lower limit of the normal range is20ms.The myocardial MR images consits of conventional bright-blood imaging and black-blood imaging, and both of them depend on the blood and MR acquirements. The black-blood imaging uses the pulse sequence to detect the slowly moving hydrogen nuclei in the myocardium. Due to the flowing blood, the blood has low signal, nearby tissue show high signal and generate an enhanced image. The bright-blood technique could image rapid flow of blood in the cardiac chambers and vessel. A single-slice breath-hold bright-blood T2*method has demonstrated good inter-study and inter-centre reproducibilities in MIC assessment and is widely employed due to its speed, sensitivity, and wide availability. Compared with the bright-blood technique, the black-blood T2*technique has superior reproducibility because this technique avoids signal contamination from blood and shows a clearer definition of the myocardial boundary due to suppressed blood signal and reduced imaging artifacts. In this paper, we aimed to develop a fully automated method to extract IS from black-blood myocardial T2*images for assessing MIC. We make five contriutions as follows:(1) The circular Hough transformation and constrinted fuzzy C-means were introduced to locate the contours of the epicardium and endocardium of the left ventricular as the initialization for the improved CV model automatically, and this avioded to the initial curve placement manually and reduced the time of the curve moving toward the boudary of the object;(2) We utilize the present Gaussian Chan-Vese(CV) model based on entropy and local neighborhood information to extract the endocatdium and epicardium;(3) We use the thresholding method and label the connect component to exract the blood pool of the right ventricle. Due to the high contras between myocardium and blood pool, we utilize the segmented left ventricle to determine an threshold value automatically for extracting the blood pool of the right ventricle;(4) We utilize the anatomical information to detect the base and apex of the interventricular septum and extract the interventricular septum;(5) The T2*values were measured by fitting the average signal of the interventricular septum to the monoexponential model.(6) Reproducibility analyses were assessed using the mean and standard deviation (SD) of the differences as described by the Bland-Altman analysis. For accessing the intra-and inter-observer variability, the coefficient of variation (CoV) was defined as the SD of the differences between the two independent measurements divide by their means and presented as a percentage.A comprehensive framework for automated IS extraction from black-blood myocardial images is proposed and the myocardial T2*values measured using the proposed method avoided the subjectivity from the worldwide operators and improved the efficiency of data processing. A total of144thalassemia major patients (age range11-51years,73males) were scanned with a black-blood multi-echo gradient-echo sequence using a1.5T Siemens Sonata system at Royal Brompton Hospital, London, UK. The proposed AISS method is fully automated, and it takes approximately1.4s to fulfill the T2*analysis of one dataset, and the T2*measurements using the AISS method were in good agreement with those manually measured by experienced observers with a mean difference of1.71%and a CoV of 4.15%. In general, a CoV less than10%is considered high reproducibility, while a CoV less than5%indicates very high reproducibility. The small T2*overestimation (0.18ms) of the AISS method over the manual method will not produce significant differences in the clinical MIC grading. The T2*measurements using the AISS method were in good agreement with those manually measured by experienced observers, and the automated T2*measurements method could assist the clinician to manage the iron content in thalassemia major.