A multi-label fusion based level set method for multiple sclerosis lesion segmentation / 生物医学工程学杂志

A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape, position and other information of lesions from magnetic resonance image. First, fuzzy c-means model is applied to extract the initial lesion region. Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. The final lesion segmentation is achieved by evolving the level set contour. The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images. The proposed method helps to reduce the work of radiologists significantly, which is useful in clinical application.

Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models / 生物医学工程学杂志

Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.

Coronary vessel intimal sequence extraction based on prior boundary constraints in optical coherence tomography image / 生物医学工程学杂志

Optical coherence tomography (OCT) is a new technique applied in cardiovascular system. It can detect vessel intimal, small structure of plaque surface and discover small lesions with its high axial resolution and quantification character. Especially with the application of OCT in characterization of coronary atherosclerotic plaque, diagnosis and treatment strategy making, optimizing percutaneous coronary intervention therapy and assessment after stent planting make the OCT become an efficient tool for cardiovascular disease diagnosis and treatment. This paper presents a novel coronary vessel intimal sequence extraction method based on prior boundary constraints in OCT image. On the basis of conventional Chan-Vese model, we modified the evolutionary weight function to control the evolutionary rate of boundary by adding local information of boundary curve. At the same time, we added the gradient energy term and intimal boundary constraint term based on priori boundary condition to further control the evolutionary of boundary curve. At last, coronary vessel intimal is extracted in a sequence way. The comparison with vessel intimal, manual segmented by clinical scientists (golden standard), indicates that our coronary vessel intimal extraction method is robust to intimal boundary blur, distortion, guide wire shadow and plaque disturbs. The results of this study can be applied to clinical aid diagnosis and precise diagnosis and treatment.

Level set motion-based elastic registration of 3D CT images in radiotherapy / 中国医学装备

Objective: To apply level set motion-based elastic registration method to radiotherapy CT image, then to provide technical support for the accurate evaluation of tumor and changing process of patients with endanger organ and its accumulative dose. Methods: Based on Vemuri’s level set motion method, we wrote the algorithm program using Matlab software and applied on two sets of 3D CT images from patients with cervical cancer and NPC for fully automatic elastic registration. Results: Comparing CT images before and after registration, for the cervical cancer patient, the minimum mean square error (MSE) decreased by 55.1%and correlation coefficient (CC) increased by 5.3%. For the NPC patient, MSE decreased by 32.1%and CC increased by 4.6%. Conclusion: From the image difference and evaluation parameters, the efficacy of level set motion-based elastic registration method was preliminarily demonstrated. In order to apply this method to clinical radiotherapy, dit needs to find a more accurate mathematical algorithm further in order to compute human anatomy deformation through image motion.

Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation

ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%.

Automatic Four-Chamber Segmentation Using Level-Set Method and Split Energy Function / 대한의료정보학회지

OBJECTIVES: In this paper, we present an automatic method to segment four chambers by extracting a whole heart, separating the left and right sides of the heart, and spliting the atrium and ventricle regions from each heart in cardiac computed tomography angiography (CTA) efficiently. METHODS: We smooth the images by applying filters to remove noise. Next, the volume of interest is detected by using k-means clustering. In this step, the whole heart is coarsely extracted, and it is used for seed volumes in the next step. Then, we detect seed volumes using a geometric analysis based on anatomical information and separate the left and right heart regions with the power watershed algorithm. Finally, we refine the left and right sides of the heart using the level-set method, and extract the atrium and ventricle from the left and right heart regions using the split energy function. RESULTS: We tested the proposed heart segmentation method using 20 clinical scan datasets which were acquired from various patients. To validate the proposed heart segmentation method, we evaluated its accuracy in segmenting four chambers based on four error evaluation metrics. The average values of differences between the manual and automatic segmentations were less than 3.3%, approximately. CONCLUSIONS: The proposed method extracts the four chambers of the heart accurately, demonstrating that this approach can assist the cardiologist.

A new modified watershed algorithm for image segmentation in cervical cancer cell adhesion / 军事医学

Objective To investigate an effective algorithm for image segmentation in cervical cancer cell adhesion , which enables accurate segmentation of the contour of adherent cells .Methods The images of target cells were extracted from the background area using level set methods , normalized with minimum values of transformation algorithms ,and multi-plied by the gradient image points in the region of interest ( ROI) to inhibit the undesired gradient information before the im-ages of adherent cells were segmented using labeled watershed algorithm .Results and Conclusion Compared to conven-tional watershed segmentation methods , this algorithm is not only effective in image segmentation of adherent cervical cancer cells with uneven staining and more accurate segmentation lines established around the contours of adherent cells , but of high clinical value .

Fast 3D Medical Image Segmentation Based on CUDA / 中国医学物理学杂志

Objective: 3D segmentation is an important part of medical image analysis and visualization. It also continues to be large challenge in the medical image segmentation. While level sets have demonstrated a great potential for 3D medical image segmentation, these algorithms have a large computational burden thus are not suitable for real time processing requirement. To solve this problem, we propose a parallel accerelated method based on CUDA. Methods: We implement C-V level set algorithm in the CUDA environment which is the NVIDIA's GPGPU model.The segmentation speed can greatly improved by using independence of image pixel and concurrence of partial differential equation .The paper shows the flow chart of the parallel computing and gives the detailed introduction of the C-V level set algorithm which is implemented in the CUDA environment. Results: Realizing the C-V level set parallel accerelated algorithm. This method has faster segmentation speed while preserving the qualitative results, Conclusions: This method is viable and makes the fast 3D medical image segmentation come hue.

Extraction of Breast Tumor Boundary in Ultrasonic Image Based on Improved Variational Level Set Algorithm / 航天医学与医学工程

Objective To propose an extraction algorithm for extraction of breast tumor boundary in its ultrasonic image. Methods A novel Global-Local Energy function was proposed in the variational Level Set formulation for the curve evolution. By enhancing the intensity of local information, the breast tumor boundary was obtained from ultrasonic images accurately. Results Experiments on a synthetic ultrasound image showed that this method was more accurate at segmenting the object from the background than the Chan-Vese algorithm based on the global optimization. Experiments on 103 ultrasonic breast tumor images also showed that the boundaries were extracted accurately with this method without any pre-processing step. Conclusion This proposed method is expected for the fast and accurate boundary extraction of ultrasonic breast tumor boundary from ultrasonic image.

An Automatic Algorithm for Image Segmentation in Urine Sediment Examination / 航天医学与医学工程

1.8?104 s)while the Mumford-Shah model based Level Set algorithm was much faster(5.42 s).Conclusion The Mumford-Shah model based Level Set algorithm can achieve urine sediment examinations accurately with both fast speed and strong robustness to the noise.

MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM / 药物分析学报

Objective To present a novel modified level set algorithm for medical image segmentation. Methods The algorithm is developed by substituting the speed function of level set algorithm with the region and gradient information of the image instead of the conventional gradient information. This new algorithm has been tested by a series of different modality medical images. Results We present various examples and also evaluate and compare the performance of our method with the classical level set method on weak boundaries and noisy images. Conclusion Experimental results show the proposed algorithm is effective and robust.

FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE / 药物分析学报

Objective To propose an automatic framework for segmentation of brain image in this paper. Methods The brain MRI image segmentation framework consists of three-step segmentation procedures. First, Non-brain structures removal by level set method. Then, the non-uniformity correction method is based on computing estimates of tissue intensity variation. Finally, it uses a statistical model based on Markov random filed for MRI brain image segmentation. The brain tissue can be classified into cerebrospinal fluid, white matter and gray matter. Results To evaluate the proposed our method, we performed two sets of experiments, one on simulated MR and another on real MR brain data. Conclusion The efficacy of the brain MRI image segmentation framework has been demonstrated by the extensive experiments. In the future, we are also planning on a large-scale clinical evaluation of this segmentation framework.

3D reconstruction of HIFU lesion area based on level set / 医疗卫生装备

Objective To design a computer aided method in HIFU treatment system to monitor the size,shape and location of HIFU lesion automatically.Methods According to ultrasound image's characters of high noise,low resolution and weak edge,a double zero level set,double speed approach was proposed to extract the contour of organ and lesion area.The serial "lice" contours were used to reconstruct three dimension(3D) HIFU lesion area in the sample.Results Experiments show that level set contour extraction method based 3D reconstruction is helpful in monitoring the size,shape and location in the organ.Conclusion This method has the feasibility to be introduced as an auxiliary technique monitoring HIFU lesion area automatically.

Multiphase Segmentation of Liver Lesion in CT Images Based on C-V Model / 医疗卫生装备

Objective To study a semi-automatic segmentation framework for liver complex lesion in CT images. Methods A multiphase level set method of image segmentation based on C-V model was proposed, which was in connection with the complex information in the pathological changes area of liver. Depending on the overall characteristics of the image, the active curve contour of C-V model stopped on the edge of objects. It was possible for the segmentation of multi-objects because multi-phase level set was drawn into. Simultaneously, the problem of overlapping and hollow caused by more level set function was avoided. Results The method behaved well in the segmentation of CT images of the liver lesion, two different area were separated. Conclusion The segmentation tests prove that the proposed segmenting method makes a good result.

Survey of Medical Image Segmentation Based on Deformable Models / 医疗卫生装备

The classical deformable models and some new approaches for the past few years are surveyed,especially de-scribe two important methods of them: snake model and level-set model.Image segmentation is the bases of 3D recon-struction technology and medical visualization image,which are meaningful to disease diagnosis and therapy in clinical.It is not only a key step and critical technology in medical image processing and image analysis but also a classic puzzle.With the need of application,it is very important to continually research the image segmentation,to increasingly improve the old approaches and introduce the new and more effective ones.