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Medical image segmentation has an important position in the medical field. People still can't find a way that can suit all kinds of medical image segmentation because of the image characteristics are more complex than the usual image. Sundry traditional methods have its boundedness. This essay presents a fire new method of CT image segmentation that combines with various methods. Firstly with the...
In this paper, a texture-based segmentation method of the Malignant Pleural Mesothelioma from thoracic CT scans is presented. For the texture analysis part, we have used an automatic sampling and a manual sampling to extract statistical features from the MPM texture. For the segmentation stage, the method iterates the whole CT volume and selects pixels satisfying the extracted statistical criteria...
Delineation of blurry boundary from medical images is challenging in particular when the target object or region of interest is adjacent to other tissues with similar or overlapping intensity distributions. To address this challenge, we propose a graph model with adaptive global and geodesic constraints to contour the indistinct boundary from CT images. The global factor reflects the appearance affinities...
Over the years, the growth in medical image processing is increasing in a tremendous manner. The rate of increasing diseases with respect to various types of cancer and other related human problems paves the way for the development in biomedical research. Thus processing and analyzing these medical images is of high importance for clinical diagnosis. This work focuses on performing effective classification...
The main objective of this work is to segment the medical image under various conditions and different backgrounds. Image segmentation is very useful and it improves the results of image analysis. Segmentation done manually is not an easy task also it consume a lots of time. Its accuracy percentage is also very less. So, there is a necessity of developing accurate and efficient algorithms for medical...
The segmentation of the bone in HR-pQCT (High Resolution peripheral Quantitative Computed Tomography) images remains a challenging task due to the image characteristics and the complex structure of the bone (cortical and trabecular). In this paper, we address the problem of separating the cortical bone from the background and the trabecular bone. We propose a novel approach to segment the cortical...
The segmentation of the rib cage in CT images represents a task of primary importance in medical imaging for different reasons. From the study of the segmented bone several features can be extrapolated. These features are indices of the presence of some diseases such as the Malignant Pleural Mesothelioma (MPM) which is the main focus of our research. This tumor is generally located very close to the...
From the preoperative partial nephrectomy planning perspective, it is essential to expose separately different kidney structures and to analyze their mutual topological relations. Only then, the identification of possible conflicts prior to surgical intervention can be facilitated. To enable this, we propose a segmentation frameworks for renal vascular tree, kidney and pelvicalyceal system from corresponding...
In this paper, a novel kidney segmentation method for Computed Tomography patient data with kidney cancer is proposed. The segmentation process is based on Hybrid Level Set method with elliptical shape constraints. Using segmentation results, a fully automated technique of kidney region classification is introduced. Identification of the kidney, tumor and vascular tree is based on RUSBoost and the...
In this study, similarity rates of the liver images are determined using 3D geometric transformation methods and numerical comparisons are made. Three geometric transformation methods scaling, rotating, and translating are consecutively applied to 10 intact liver images which are drawn by the radiologists. Atlases of liver images are generated, Dice coefficients are calculated according to the specified...
The segmentation of the whole heart is quite necessary in computer-aided diagnosis. However, traditional medical image segmentation algorithms cannot achieve whole heart segmentation of CT sequence images accurately. In this study, two improved segmentation algorithms for whole cardiac CT sequence images are developed, as the correlation among adjacent slices of CT sequence images is considered, the...
A method based on two passes of 3D region growing and morphological reconstruction for segmenting pulmonary airway tree from computed tomography (CT) chest scans is presented to solve the problem of leakage and under-segmentation caused by the partial volume effect and motion artifact. Firstly, the first pass of 3D region growing with optimal threshold range is used to extract the rough airway. Then,...
The global increase in population has simultaneously raised the awareness to maintain good health in most of the people. The poor quality of food taken and environmental pollution leads to occurrence of lung cancer in most of the people. It is highly important to detect the lung cancer in earlier stages with minimum time delay and provide a better solution to reduce the lung cancer. Early detection...
Due to the weak boundary, narrow or even disappeared joint space and varying topology in challenging CT, accurate segmentation of the femur from hip joint is still a difficult task. To address this problem, the proposed method combines anatomical information of relative location of bone tissues and neighboring slices to predict a statistical model for Joint space identification. A novel idea in this...
Level set models are widely used in image processing and computer vision for segmentation. In this paper, an improved geometric active contour model is used for the segmentation of abdominal organs in abdomen CT images. The pre-processing of input images was done by anisotropic diffusion filter that effectively preserve the edges. The proposed Distance Regularized Level set Evolution (DRLSE) doesn't...
Segmentation of the pelvic bone from computed tomography (CT) images is a challenging task as CT images are noisy, have wide inhomogeneities in the intensity distribution and its anatomical structure is quite complex. In this paper a framework is proposed to automatic segmentation and 3D visualization of the pelvic bone from computed tomography images to assist the physician to take medical decisions...
A brain tumour is a growth of cells in the brain that multiplies in an abnormal and uncontrollable way. The estimation of brain tumour volume is important for diagnosis and treatment process. The computed tomography is one of the most important devices used for detection, diagnosis, and volume estimation of the brain tumour. The most common disadvantage of this device is the high radiation dose that...
Medical images are being utilized progressively inside the healthcare services for diagnosis, guiding treatment, planning treatment and checking illness progression. In fact, medical imaging chiefly processes uncertain, lost, vague, complementary, conflicting, redundant contradictory, distorted information and data has powerful structural character. As a general approach, the comprehension of any...
Segmentation is an important process especially in a Computer Aided Diagnosis (CADx) system. There are various methods of segmentation. A large majority of these involve a threshold based approach. For this study full HRCT Thorax scans from 10 normal patients were analysed. This study proposes a performance evaluation system for segmentation system. The performance evaluation uses five measures which...
Computer assisted diagnosis systems (CADs) is now commonly used as a second opinion to help radiologists in image interpretation by emphasizing on the suspicious areas. Segmentation of region of interests in 2-dimensional (2D) or volume of interests in 3-dimensional (3D) images is a critical step in CAD systems. 3D image segmentation using 2D slices has been a keen of interest for research purpose...
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