Medical Image Segmentation Algorithm Research Articles

AB0957 Computer Assisted Osteosclerosis Detection A Useful Tool for Doctor

BackgroundOsteosclerosis is widely known as a radiological sign of osteoarthritis. Nowadays the classical film radiography is more often replaced by the digital medical imaging.ObjectivesTo find out if the osteosclerosis is...

Medical Image Segmentation Methods, Algorithms, and Applications

ABSTRACTMedical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.

An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

Fuzzy Clustering Algorithms for Effective Medical Image Segmentation

Medical image segmentation demands a segmentation algorithm which works against noise. The most popular algorithm used in image segmentation is Fuzzy C-Means clustering. It uses only intensity values for clustering which makes it highly sensitive to noise. The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-Means (IFCM), and Type-II Fuzzy C-Means (T2FCM) is presented in this paper. These algorithms are executed in two scenarios- both in the absence and in the presence of noise and on two kinds of images- Bacteria and CT scan brain image. In the bacteria image, clustering differentiates the bacteria from the background and in the brain CT scan image, clustering is used to identify the abnormality region. Performance is analyzed on the basis cluster validity functions, execution time and convergence rate. Misclassification error is also calculated for brain image analysis.

Medical Image Segmentation

Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms. In this paper, image segmentation algorithms are divided into classical image segmentation algorithms and segmentation methods combined with certain mathematical tools, including threshold segmentation methods, image segmentation algorithms based on the edge, image segmentation algorithms based on the region, image segmentation algorithms based on artificial neural network technology, image segmentation algorithms based on contour model and image segmentation algorithm based on statistical major segmentation algorithm and so on. Finally, the development trend of medical image segmentation algorithms is discussed.

CS-LBF Improved Model for 3D Medical Image Segmentation

This paper presents an algorithm for three-dimensional medical image segmentation based on the Contrast and Shape Constrained Local Binary Fitting improved model. Due to Local Binary Fitting model is sensitive to initialization and easy to fall into local extreme value, the new algorithm adds contrast constraint term to the Local Binary Fitting model, aiming at solving the common existed problem of inconsistent brightness and low contrast ratio. Adding shape constraint term can improve the original Local Binary Fitting model by constructing shape constraint energy field around the average shape by the level set method to deal with the leak of deformation curve. In order to promote the speed of model evolution, the kernel function is simplified. Two-dimensional Contrast and Shape Constrained Local Binary Fitting model is then extended to three-dimensional and a three-dimensional dental pulp image is segmented. Experimental results show that the segmentation accuracy, the connection degree and the efficiency of the new method are greatly improved compared to original LBF model.

Fast FCM with Spatial Neighborhood Information for Brain Mr Image Segmentation

Abstract Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is decreased and neighborhood spatial information is exploited in FFCM. Moreover, iteration numbers by proposed FFCM/sFFCM techniques are decreased efficiently. The FCM/FFCM techniques are examined on both simulated and real MR images. Furthermore, to considerably decrease of convergence time and iterations number, cluster centroids are initialized by an algorithm. Accuracy of the new approach is same as standard FCM. The quantitative assessments of presented FCM/FFCM techniques are evaluated by conventional validity functions. Experimental results demonstrate that sFFCM techniques efficiently handle noise interference and significantly decrease elapsed time.

Application of Segmentation Techniques on Atherosclerosis Images

This paper presents application of segmentation techniques on atherosclerosis images using various segmentation methods like Otsu thresholding, fuzzy C means, clustering algorithm and marker controlled watershed segmentation algorithm. Atherosclerosis is one of the causes of coronary heart disease (CHD). The proposed marker controlled watershed algorithm for medical image segmentation and analysis is very important because of its advantages, such as always being able to construct an entire division of the color image and prevent over segmentation as compared to conventional watershed algorithm. Paper finds Region of Interest (ROI) values of segmented image with proposed technique for coronary atherosclerosis. Keywords - Coronary heart disease (CHD), Clustering Image Segmentation Technique, Medical Image Segmentation, Fuzzy C means algorithm, Marker based Watershed Image Segmentation Technique.

A Hybrid Region Growing Algorithm for Medical Image Segmentation

In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.

Research and Improvement of Live-Wire Interactive Algorithm for Medical Image Segmentation

Traditional Live Wire algorithm distinguished the strength edge of objectives uneasily and executive speed of algorithm is slow. For these problems, an improved Live-Wire algorithm is proposed. First it implements anisotropic diffusion filtering to images and constructs a new expense function, then combined with region growing segmentation algorithm, it implements interactive segmentation to medical images. Improved algorithm avoids the shortcomings of the traditional Live-wire algorithm which is sensitive to noise and can not effectively distinguish the edge of the strength, also reduces the time and blindness of dynamic programming to find the optimal path and improves the accuracy and implementation efficiency of the image segmentation.

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.

An Enhanced Medical Images Segmentation Using Skey Gaussian Mixture Model and Hierarchical Clustering Algorithm

Image segmentation is an important process in the field of medical imaging.Recently, much work has been reported in medical image segmentation. Among these techniques, finite Gaussian mixture models are considered to be more recent and accurate. However, in this approach, a number of segments that an image can be divided are taken through apriori and if these segments are not initiated properly it leads to misclassification. Hence, to overcome this disadvantage, we proposed an algorithm for Medical Image Segmentation using Hierarchical Clustering and Skew Gaussian Mixture. The experimentation is done with two different brain images and the results obtained are evaluated using both Image Quality metrics and Segmentation Quality Metrics.

Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI). For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF) and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM) and kernelized fuzzy C-means with spatial constraints (SKFCM). These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

Segmentation of Medical Image using Clustering and Watershed Algorithms

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to construct an entire division of the image. On the other hand, its disadvantages include over segmentation and sensitivity to false edges. Approach: In this study we proposed a methodology that integrates K-Means clustering with marker controlled watershed segmentation algorithm and integrates Fuzzy C-Means clustering with marker controlled watershed segmentation algorithm separately for medical image segmentation. The Clustering algorithms are unsupervised learning algorithms, while the marker controlled watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. Results: In this study, we compared K-means clustering and marker controlled watershed algorithm with Fuzzy C-means clustering and marker controlled watershed algorithm. And also we showed that our proposed method produced segmentation maps which gave fewer partitions than the segmentation maps produced by the conservative watershed algorithm. Conclusion: Integration of K-means clustering with marker controlled watershed algorithm gave better segmentation than integration of Fuzzy C-means clustering with marker controlled watershed algorithm. By reducing the amount of over segmentation, we obtained a segmentation map which is more diplomats of the several anatomies in the medical images.

Generalized rough fuzzy c-means algorithm for brain MR image segmentation

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations.

SU‐D‐214‐06: Designing Simulators for Studying Medical Physics: From CT Imaging to Radiotherapy Treatment Planning and Quality Assurance

Purpose: Designing simulators for studying medical physics ranging from CT imaging to radiotherapy treatment planning and quality assurance will be very valuable for students, educators, and medical professionals to learn and teach radiation physics principles and to perform virtual practicing of the patient diagnosis and treatment based on physical principles. Methods: A Monte Carlo method is the best to simulate the radiation transport phenomena. Thus, in this project, we used our in‐house Monte Carlo code, PMCEPT code, to simulate directly the interactions between the radiation and the patient body. However, for better understanding of the complex physical phenomena involved in the radiation interactions with matter, some augmenting novel computational approaches such as ray‐tracing and medical image segmentation algorithms are included. Specifically, fast ray tracing method is useful for better understanding the CT images and the near future IGRT treatment planning. For the real time simulations, parallel algorithms adaptive to PC cluster and GPU‐accelerated algorithms were employed. Results: The CBCT simulator with a set of ray beams emitting towards every pixel center of the detector from the X‐ray source was developed. Semi‐automatic segmentation of CT image using watershed algorithm and graphical user interface was developed for analyzing the DICOM data to provide ASCII data file which is readable. The external beam accelerator operated with keyboard and mouse was developed. Conclusions: The important benefits of these virtual simulators may be the replacement of the conventional laborious procedures which are required for the expensive hardware simulator units, the efficiency increment, the accuracy improvement of radiation treatment procedure, and the cost reduction in terms of time and physical patientˈs presence. A more extensive assessment of the efficiency improvement provided by these simulators is still under investigation and will be presented in future articles.This research was supported by BSR program through NRF funded by the MEST (grant # 2010‐0003983)

Adaptive Medical Image Segmentation Algorithm Combined with DRLSE Model

The distance regularized level set evolution model proposed recently which uses Gaussian filter to reduce the image noise will yield blurred image edges. Besides, the model still can’t segment adaptively. To slove these problems, we adopt regularized P-M equation filter which can remove the noise while preserve edge information. On the other hand, gradient informarion which depends on inward and outward area's curve is used to improve the direction of the unit normal vector. As a result, the curve can realize adaptive evolution inwards or outwards. In the end, the modified algorithm is applied to accurately extract the medical image's contour, and greatly reduce computational cost.

New medical image segmentation algorithm based on subregion similarity

Introducing the traditional region growing method into a contour approximating method, a new segmentation algorithm based on subregion similarity was presented. Firstly, an initial polygon contour was brought in by human-computer interactive process. Then the region of interest was obtained by using a sub-region similarity which served as driving force of the contour evolution. The experimental results show that the algorithm is of more anti-noise ability than the region growing method, and can divide up the region of interest efficiently.

Suggested Algorithm for medical Image Segmentation Using Fractal geometry

Texture extraction and analysis are regarded as important operations in image processing field for various computer applications. In this research, fractional dimension has been used in the process of texture extraction and analysis of various medical images. 2D Variation and Box Counting algorithms were used to extract the textured image and segment the radiation image based on the extracted textures. This research first adopted common and used methods in 2D variation algorithm, as well as a proposed method to adopt this algorithm. Practical experiments showed the efficiency of the proposed method in the process of texture extraction with execution time less than the time required by other common used methods.

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.