Medical Image Segmentation Methods Research Articles

Medical image segmentation using rough set and local polynomial regression

Rough-set based multimodal histogram thresholding technique is effective for medical image segmentation. However, it is difficult to obtain the significant peaks and valleys of the roughness measure. Moreover, it is sensitive to the noise for medical image. In this paper, we proposed a new medical image segmentation method using rough set theory and local polynomial regression model to address those disadvantages. Firstly, compute histogram of image intensity information and histon of image intensity and spatial information. Secondly, use the local polynomial regression model to smooth the histogram and histon. The smoothed histogram correlates with lower approximation and the smoothed histon correlates with upper approximation. Lastly, rough measure is calculated with the lower and upper approximations. And then, multimodal thresholding method is applied to medical image segmentation. The local polynomial regression model can obtain a smooth rough measure and has two advantages: first, it is easy to find the real peaks and valleys of the smoothed roughness measure to segment medical image; second, the local polynomial regression reduces the effect of noise and can find the thresholds correctly. The proposed approach is compared with the histogram based approach, histon based approach, and rough set with the histogram and histon based approach. Experimental results demonstrate that our approach can find the real peaks and valleys more easily and yields better segmentation results than those of other three methods.

An efficient neural network based method for medical image segmentation

The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.

A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation

Appropriate initialization and stable evolution are desirable criteria to satisfy in level set methods. In this study, a novel region-based level set method utilizing both global and local image information complementarily is proposed. The global image information is extracted from mean shift clustering without any prior knowledge. Appropriate initial contours are obtained by regulating the clustering results. The local image information, as extracted by a data fitting energy, is employed to maintain a stable evolution of the zero level set curves. The advantages of the proposed method are as follows. First, the controlling parameters of the evolution can be easily estimated by the clustering results. Second, the automaticity of the model increases because of a reduction in computational cost and manual intervention. Experimental results confirm the efficiency and accuracy of the proposed method for medical image segmentation.

A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation

The fuzzy C-means (FCM) algorithm has significant importance compared to other methods in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially in the presence of intensity inhomogeneity in MRI. Main reason is that a single fuzzifier in FCM cannot properly represent pattern memberships for all clusters. In this paper, we present a novel algorithm for fuzzy segmentation of MRI data. The algorithm utilizes two fuzzifiers used in interval type-2 FCM and a spatial constraint on the membership functions. Also, in our investigation, validity functions are extended to generalized form for interval type-2 fuzzy clustering. The experimental results on both synthetic and MR images show that the proposed algorithm has better performance on image segmentation than conventional FCM based algorithms.

A robust medical image segmentation method using KL distance and local neighborhood information

In this paper, we propose an improved Chan–Vese (CV) model that uses Kullback–Leibler (KL) distances and local neighborhood information (LNI). Due to the effects of heterogeneity and complex constructions, the performance of level set segmentation is subject to confounding by the presence of nearby structures of similar intensity, preventing it from discerning the exact boundary of the object. Moreover, the CV model cannot usually obtain accurate results in medical image segmentation in cases of optimal configuration of controlling parameters, which requires substantial manual intervention. To overcome the above deficiency, we improve the segmentation accuracy by the usage of KL distance and LNI, thereby introducing the image local characteristics. Performance evaluation of the present method was achieved through experiments on the synthetic images and a series of real medical images. The extensive experimental results showed the superior performance of the proposed method over the state-of-the-art methods, in terms of both robustness and efficiency.

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.

GC-ASM: Synergistic integration of graph-cut and active shape model strategies for medical image segmentation

Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF>96%, FPVF<0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.

Efficient Segmentation of Medical MRI Image using WT-WS Algorithm

Image segmentation is an important processing step in many image, video and computer vision applications especially in medical field. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than the other. Up to date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of tumor detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is an important step in contouring during radiotherapy planning. This paper provides new hybrid medical image segmentation method based on Watershed and Wavelet Transform. In this paper, we propose an algorithm for segmentation problems in medical imaging modalities especially for brain Magnetic Resonance Images MRI). The watershed transformation is a useful morphological segmentation tool used for a variety of greyscale images. The segmentation procedure consists of pyramid representation, image segmentation, region merging and region projection. Each layer is split into a number of regions by rooting, labeling technique and the boundary is extracted by threshold and the image is smoothened by wavelet transform. Experimental results prove that the proposed algorithm is comparatively better than the existing systems .

Medical Image Segmentation Algorithm Based on Granular Computing

Granular Computing theory is a interesting research direction in artificial intelligence field. In this paper, granular computing theory is applied to medical image segmentation. Granularity thinking in image segmentation is expounded, and a novel medical image segmentation method is proposed. Firstly, we construct different granularities according to different features that the image contained, secondly, do the attributes combination to the obtained quotient spaces according to the quotient space granularity synthesis principle, and then complete the image segmentation. Compared with the methods adopting single image feature, this method may fully use the image information in a more effective way and may obtain better segmentation effects.

Adaptive multi-objective archive-based hybrid scatter search for segmentation in lung computed tomography imaging

This article proposes a multi-objective clustering ensemble method for medical image segmentation. The proposed method is called adaptive multi-objective archive-based hybrid scatter search (AMAHSS). It utilizes fuzzy clustering with optimization of three fitness functions: global fuzzy compactness of the clusters, fuzzy separation and symmetry distance-based cluster validity index. The AMAHSS enables the search strategy to explore intensively the search space with high-quality solutions and to move to unexplored search space when necessary. The best single solution is processed using the metaclustering algorithm. The proposed framework is designed to segment lung computed tomography images for candidate nodule detection. This candidate nodule will then be classified as cancerous or non-cancerous. The authors validate the method with standard k-means, fuzzy c-means and the multi-objective genetic algorithm with different postprocessing methods for the final solution. The results obtained from the benchmark experiment indicate that the method achieves up to 90% of the positive predictive rate.

Novel Method of MRI Medical Image Segmentation Combining Watershed Algorithm and WKFCM Algorithm

Aimed at the disadvantage of over-segmentation that traditional watershed algorithm segmented MRI images, a new method of MRI image segmentation was presented. First, through traditional watershed segmentation algorithm, the image was segmented into different areas, and then based on the improved kernel-clustering algorithm, we used Mercer-kernel to map average gray value of each area to high-dimensional feature space, making originally not displayed features manifested. In this way, we can achieve a more accurate clustering, and solve over-segmentation problem of watershed algorithm segmenting MRI images efficiently, thereby get better segmentation result. Experimental results show that the method of this paper can segment brain MRI images satisfactorily, and obtain clearer segmentation images.

Medial-Based Deformable Models in Nonconvex Shape-Spaces for Medical Image Segmentation

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images.

Interactive 3D medical image segmentation with energy-minimizing implicit functions

We present an interactive segmentation method for 3D medical images that reconstructs the surface of an object using energy-minimizing, smooth, implicit functions. This reconstruction problem is called variational interpolation. For an intuitive segmentation of medical images, variational interpolation can be based on a set of user-drawn, planar contours that can be arbitrarily oriented in 3D space. This also allows an easy integration of the algorithm into the common manual segmentation workflow, where objects are segmented by drawing contours around them on each slice of a 3D image.Because variational interpolation is computationally expensive, we show how to speed up the algorithm to achieve almost real-time calculation times while preserving the overall segmentation quality. Moreover, we show how to improve the robustness of the algorithm by transforming it from an interpolation to an approximation problem and we discuss a local interpolation scheme.A first evaluation of our algorithm by two experienced radiology technicians on 15 liver metastases and 1 liver has shown that the segmentation times can be reduced by a factor of about 2 compared to a slice-wise manual segmentation and only about one fourth of the contours are necessary compared to the number of contours necessary for a manual segmentation.

Optimum Regularized Joint Registration and Segmentation Method for Medical Brain Images

Image registration and segmentation are the two important processes that are frequently used in medical image processing and computer vision applications. In traditional medical image applications both the techniques are applied independently even though the solution to one impacts the solution of the other. Currently medical image segmentation is very complex task due to the lack of sufficient contrast, SNR, and volume averages caused due to the non-uniform magnetic field. The problem is still high with MRI scans rather than other scans due to lack of real boundary. Availability of sophisticated diagnostic methods in the medical domain, demands the fusion of information from different sources for the better analysis. Similarity is enhanced by performing the non-rigid registration, where the local registration highly depends on segmentation of objects. This paper deals with the Atlas-based segmentation technique requires that the given atlas image is to be registered with the target image to find the desired shape segmentation in the target image. This paper discus the joint registration and segmentation process is achieved through highly accurate variational cost effective Distance Regularized Level Set Evolution (DRLSE) method for medical scan images. The key features of this algorithm are, it can accurately converge towards sharp object boundary corners due to forward and backward diffusion and also applied for small and large deformations. It uses less computational cost due to large time steps. Keywords: Medical Image Processing, Image Registration, Segmentation, Joint Registration and Segmentation, Distance Regularized Level Set Evolution, Deformations, Convergence, Computational time.

Medical image segmentation based on non-parametric mixture models with spatial information

Because of too much dependence on prior assumptions, parametric estimation methods using finite mixture models are sensitive to noise in image segmentation. In this study, we developed a new medical image segmentation method based on non-parametric mixture models with spatial information. First, we designed the non-parametric image mixture models based on the cosine orthogonal sequence and defined the spatial information functions to obtain the spatial neighborhood information. Second, we calculated the orthogonal polynomial coefficients and the mixing ratio of the models using expectation-maximization (EM) algorithm, to classify the images by Bayesian Principle. This method can effectively overcome the problem of model mismatch, restrain noise, and keep the edge property well. In comparison with other methods, our method appears to have a better performance in the segmentation of simulated brain images and computed tomography (CT) images.

A novel active contour model for medical image segmentation

A novel segmentation method for medical image with intensity inhomogeneity is introduced. In the proposed active contour model, both region and gradient information are taken into consideration. The former, i.e., region-based fitting energy, draws upon the region information and guarantees the accurate extraction of inhomogeneous image’s local information. The latter, i.e., an edge indicator, weights the length penalizing term to consider the gradient constrain. Moreover, signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function. Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.

Web-based interactive 2D/3D medical image processing and visualization software

There are many medical image processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus the data and processing power of that application are not available to other workstations. Further, there are operating system and processing power limitations which prevent such applications from running on every type of workstation. By developing web-based tools, it is possible for users to access the medical image processing functionalities wherever the internet is available. In this paper, we introduce a pure web-based, interactive, extendable, 2D and 3D medical image processing and visualization application that requires no client installation. Our software uses a four-layered design consisting of an algorithm layer, web-user-interface layer, server communication layer, and wrapper layer. To compete with extendibility of the current local medical image processing software, each layer is highly independent of other layers. A wide range of medical image preprocessing, registration, and segmentation methods are implemented using open source libraries. Desktop-like user interaction is provided by using AJAX technology in the web-user-interface. For the visualization functionality of the software, the VRML standard is used to provide 3D features over the web. Integration of these technologies has allowed implementation of our purely web-based software with high functionality without requiring powerful computational resources in the client side. The user-interface is designed such that the users can select appropriate parameters for practical research and clinical studies.

An Intelligent Image Segmentation Algorithm for Medical Ultrasonic Instruments

In company with medical instruments ' development, the corresponding software plays more and more role in the application. And medical image processing software has become an important component of the medical ultrasonic instruments. Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. But state-of-arts methods in the aspect of segmentation can not get satisfactory results. We propose an intelligent image segmentation method for medical ultrasonic images. The algorithm improved active contour model with relevance vector machine, where the advantages of supervised learning classification and the global region distribution information can be exploited to enhance the performance. In order to improve the segmentation speed and get precise initial contour, relevance vector machine also is used to obtain initial contour firstly. A large amount of experimental results have proved that our method outperforms many state-of-arts methods in the aspect of segmentation, and the method can be used in ultrasonic instruments effectively.

Live level set: a hybrid method of livewire and level set for medical image segmentation.

Livewire and level set are popular methods for medical image segmentation. In this article, the authors propose a hybrid method of livewire and level set, termed the live level set (LLS). The LLS replaces the one graph update iteration in the classic livewire with two iterations of graph updates. The first iteration generates an initial contour for a level set computation. The level set distance is then factored back into the cost function in the second iteration of graph update. The authors validated LLS using synthetic images. The results show that the performance of the LLS is superior to both the classic live wire and traditional level set methods in terms of accuracy, reproducibility, smoothness and running time. They also qualitatively evaluated the LLS using real clinical data.