Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation (original) (raw)
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Artificial Intelligence in Medicine
Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed. To increase the contrast of the liver CT images, the intensity values are adjusted and high frequencies are removed using histogram equalization and median filter approach. It is followed by transforming the CT image to NS domain, which is described using three subsets (percentage of truth T, the percentage of indeterminacy I, and percentage of falsity F). The obtained NS image is enhanced by adaptive threshold and morphological operators to focus on liver parenchyma. The enhanced NS image passed to a watershed algorithm for post-segmentation process and liver parenchyma is extracted using the connected component algorithm. Finally, the liver tumors are segmented from the segmented liver using fast fuzzy c-mean (FFCM). A quantitative analysis is carried out to evaluate segmentation results using six different indices. The results show that the overall accuracy offered by the employed neutrosophic sets is accurate, less time consuming, less sensitive to noise and performs better on non-uniform CT images.
Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization
Image segmentation is an important task in the image processing field. Efficient segmentation of images considered important for further object recognition and classification. This paper presents a novel segmentation approach based on Particle Swarm Optimization (PSO) and an adaptive Watershed algorithm. An application of liver CT imaging has been chosen and PSO approach has been applied to segment abdominal CT images. The experimental results show the efficiency of the proposed approach and it obtains overall accuracy 94% of good liver extraction.
Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 2014
An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept of fractional calculus is used to control the convergence rate of particles, wherein each one of them represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data. The experimental results based on the Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed algorithm is fast, accurate, and less time consuming.
mi.tj.chiba-u.jp
Accurate medical diagnosis requires the segmentation of a large number of medical images. Although manual segmentation provides good results, it is a costly process in terms of both money and time. Automatic segmentation, on the other hand, remains a challenge due to low image contrast and ill-defined boundaries. In this report, we propose a fully automated medical image segmentation framework in which the segmentation process is constrained by two prior models: a shape prior model and a texture prior model. The shape prior model is constructed from a set of manually segmented images using principal component analysis (PCA), while wavelet packet decomposition is used to extract the texture features. The Fisher linear discriminant algorithm is employed to build the texture prior model from the set of texture features and to perform preliminary segmentation. Then, the particle swarm optimization (PSO) algorithm is used to refine the preliminary segmentation according to the shape prior model. In this work, we tested the efficacy of the proposed technique for segmentation of the liver in abdominal CT scans. The obtained results demonstrated the efficiency of the proposed technique in accurately delineating the target objects.
A Particle Swarm based Approach for Classification of Cancer based on CT Scan
International journal of computer applications, 2019
In this paper, the technique is propose that made use of Particle Swarm Optimization (PSO) algorithm for tumor detection by using the techniques of Image Processing. This proposed algorithm is based on three steps. First, it identities the affected area, Second, it makes enhancement to the image, Finally, it performs segmentation and extraction of characteristics of the affected area. The propose approach takes any medical image of Computed Tomography CT scan, and provides indicators for physicians decision-making to build treatment plans with minimal diagnostic errors and more accurate description of the treatment plan at minimal cost. The proposed approach has been implemented and tested using data from Oncology Center (the National Center for Oncology Therapy, Hadramout El Wadi, Yemen) and shown very promising .
Detection of Brain Tumor by Particle Swarm Optimization using Image Segmentation
Indian Journal of Science and Technology, 2015
Background/Objectives: Image segmentation is one of the fundamental techniques in image processing. During past ǡ ϐ ǡ ϐ Ǣ ǡ for the patient. Methods/Statistical Analysis: This research work analyses about the detection and separation of brain tumor through Magnetic Resonance Imaging (MRI) medical images using Particle Swarm Optimization (PSO), a heuristic global optimization method based on swarm intelligence. The algorithm is widely used and rapidly developed for its ease implementation. This work has four stages that includes conversion, implementation, selection and extraction. Findings: ȋ Ȍ ϐ ϐ Ǥ ȋ Ȍ Ǥ ǡ Ǥ ϐ ϐ Ǥ Magnetic Resonance (MRI) images. Finally, this work concludes with the extraction of the resultant image, which is taken ǡ ϐ ϐ ϐǤ ϐ Ǥ Applications/Improvements: The same PSO algorithm is ϐ ϐ Ǥ using other algorithms are also considered for further implementation.
A Comparative Analysis of K-Means and Fuzzy C-Means Clustering Algorithms Based on CT Liver Image
Image processing techniques are broadly used in different areas of medical imaging to detect different types of abnormalities. The clustering algorithm is used in image processing for image segmentation. Image processing technique can help to detect the tumor and also it helps to identify the affected parts of the organs. This paper describes two clustering algorithm K-Means and Fuzzy C-Means clustering to compare their performance based on CT liver image. The segmentation result of K-Means is compared to the segmentation result of Fuzzy CMeans clustering. Experiments were conducted to evaluate their performance based on some criteria such as computational time, energy, homogeneity, PSNR etc.
An Efficient Particle Swarm Optimization for MRI Fuzzy Segmentation
2017
In this paper, we propose a novel initialization approach for the Fuzzy CMeans Algorithm (FCM) based on Fuzzy Particle Swarm Optimization (FPSO) applied to brain MR image segmentation. The proposed method, named FPSOFCM (Fuzzy Particle Swarm Optimization for FCM) uses the FPSO algorithm to get the initial cluster centers of FCM according to a new fitness function which combines fuzzy cluster validity indices. The FPSOFCM was evaluated on several MR brain images corrupted by different levels of noise and intensity non-uniformity. Experiment results show the proposed approach improves segmentation results. Key-words: fuzzy c-means (FCM), MRI segmentation, swarm intelligence, particle swarm optimization (PSO).
Procedia Computer Science, 2016
Medical Image segmentation is the most challenging problems in the research field of MRI scan analysis. Automated brain tumor segmentation and detection are eminently important in medical diagnostics because it provides information related to functional structures as well as potential abnormal tissue necessary to demarcate surgical plan. But automatic tumor segmentation is still challenging because of low contrast and ill-defined boundaries and accuracy problem. Therefore Enhanced Darwinian Particle Swarm Optimization (EDPSO) is proposed for automated tumor segmentation which overcomes the drawback of existing Particle Swarm Optimization(PSO).This innovative method consists of four steps. First step is pre-processing, film artifacts and unwanted portions of MRI images are removed using tracking algorithm .Second step involves the process of removing the noises and high frequency component using Gaussian filter. Third step, segmentation is done using Darwinian Particle Swarm Optimization and Fourth step is classification, which is done by Adaptive Neuro Fuzzy Inference System. The performance of the proposed method is systematically evaluated using the MRI brain images.