共查询到20条相似文献，搜索用时 0 毫秒
1.
P. Sivakumar P. Ganeshkumar 《International journal of imaging systems and technology》2017,27(3):265-272
Brain tumor and brain stroke are two important causes of death in and around the world. The abnormalities in brain cell leads to brain stroke and obstruction in blood flow to brain cells leads to brain stroke. In this article, a computer aided automatic methodology is proposed to detect and segment ischemic stroke in brain MRI images using Adaptive Neuro Fuzzy Inference (ANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The brain image is enhanced using Heuristic histogram equalization technique. Then, texture and morphological features are extracted from the preprocessed image. These features are optimized using Genetic Algorithm and trained and classified using ANFIS classifier. The performance of the proposed ischemic stroke detection system is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and Mathew's correlation coefficient. 相似文献
2.
Lu Liu Zhenhong Jia Jie Yang Nikola Kasabov Fellow IEEE 《International journal of imaging systems and technology》2015,25(3):199-205
In the process of medical image formation, the medical image is often interfered by various factors, and it is deteriorated by some new noise that may reduce the quality of the obtained image, which affect the clinical diagnosis seriously. A new medical image enhancement method is proposed in this article. Firstly, the initial medical image is decomposed into the NSCT domain with a low‐frequency sub‐band, and several high‐frequency sub‐bands. Secondly, linear transformation is adopted for the coefficients of the low‐frequency sub‐band. An adaptive thresholding method is used for denoising the coefficients of the high‐frequency sub‐bands. Then, all sub‐bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The results of experiment show that the proposed method is superior to other methods in image entropy, EME, and PSNR. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 199–205, 2015 相似文献
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Classification of structural brain magnetic resonance (MR) images is a crucial task for many neurological phenotypes that machine learning tools are increasingly developed and applied to solve this problem in recent years. In this study binary classification of T1‐weighted structural brain MR images are performed using state‐of‐the‐art machine learning algorithms when there is no information about the clinical context or specifics of neuroimaging. Image derived features and clinical labels that are provided by the International Conference on Medical Image Computing and Computer‐Assisted Intervention 2014 machine learning challenge are used. These morphological summary features are obtained from four different datasets (each N > 70) with clinically relevant phenotypes and automatically extracted from the MR imaging scans using FreeSurfer, a freely distributed brain MR image processing software package. Widely used machine learning tools, namely; back‐propagation neural network, self‐organizing maps, support vector machines and k‐nearest neighbors are used as classifiers. Clinical prediction accuracy is obtained via cross‐validation on the training data (N = 150) and predictions are made on the test data (N = 100). Classification accuracy, the fraction of cases where prediction is accurate and area under the ROC curve are used as the performance metrics. Accuracy and area under curve metrics are used for tuning the training hyperparameters and the evaluation of the performance of the classifiers. Performed experiments revealed that support vector machines show a better success compared to the other methods on clinical predictions using summary morphological features in the absence of any information about the phenotype. Prediction accuracy would increase greatly if contextual information is integrated into the system. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 89–97, 2017 相似文献
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R. Rajagopal 《International journal of imaging systems and technology》2019,29(3):353-359
The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non-Glioma image based on the optimized set of features. Furthermore, energy-based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy. 相似文献
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针对基于LBP的许多改进方法需要提前训练,对旋转和照明变化鲁棒性较差的特点,本文通过融合CLBP和图像表面的局部几何不变特征提出了一种新的纹理分类方法。该算法首先计算图像表面的局部几何不变特征,然后对其进行量化和编码。其次,再将编码结果与CLBP直方图进行融合。本文提出的算法能够同时提取图像的宏观和微观特征,且具有不明显增加特征维度,无需提前训练,对图像的旋转和光照变化保持不变的特点。在两个标准纹理数据库上进行实验验证,结果表明,本文算法与其它算法相比在分类精度和鲁棒性上都有明显的提高。 相似文献
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将纹理特征与波形特征用于LiDAR数据分类,进行了纹理特征与波形特征的最佳组合方案研究。首先将LiDAR全波形数据的高程、波宽、振幅和回波次数等波形特征信息转化为波形特征图像;然后利用灰度直方图和灰度共生矩阵(GLCM)提取多种纹理特征,并与波形特征图像叠加构成多维特征图像;最后讨论纹理特征与波形特征组合对分类的影响,并确定最佳组合方案,探讨不同分类器对纹理与波形特征组合的适应性。实验结果表明,某些纹理特征能够提高分类精度,但不是分类特征越多越好,只有最佳组合才能充分利用纹理和波形特征,提高分类精度。 相似文献
7.
Jasmine Hephzipah Johnpeter Thirumurugan Ponnuchamy 《International journal of imaging systems and technology》2019,29(4):431-438
The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy. 相似文献
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Heba M. El-Hoseny Wael Abd El-Rahman Walid El-Shafai El-Sayed M. El-Rabaie Korany R. Mahmoud Fathi E. Abd El-Samie Osama S. Faragallah 《International journal of imaging systems and technology》2019,29(1):4-18
In the current era of technological development, medical imaging plays an important part in several applications of medical diagnosis and therapy. This requires more precise images with much more details and information for correct medical diagnosis and therapy. Medical image fusion is one of the solutions for obtaining much spatial and spectral information in a single image. This article presents an optimization-based contourlet image fusion approach in addition to a comparative study for the performance of both multi-resolution and multi-scale geometric effects on fusion quality. An optimized multi-scale fusion technique based on the Non-Subsampled Contourlet Transform (NSCT) using the Modified Central Force Optimization (MCFO) and local contrast enhancement techniques is presented. The first step in the proposed fusion approach is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The NSCT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion framework is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation (STD), edge intensity, entropy, peak signal-to-noise ratio, Q ab/f, and processing time. Experimental results demonstrate that the proposed optimized NSCT medical image fusion approach based on the MCFO and histogram matching achieves a superior performance with higher image quality, average gradient, edge intensity, STD, better local contrast and entropy, a good quality factor, and much more details in images. These characteristics help for more accurate medical diagnosis in different medical applications. 相似文献
9.
N. Herald Anantha Rufus D. Selvathi 《International journal of imaging systems and technology》2017,27(3):273-280
The abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co‐Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation. 相似文献
10.
基于视觉底层特征的图像增强方法 总被引：1，自引：1，他引：0
目的提出基于视觉底层特征对不同类型图像权重的图像增强方法。方法提取图像的视觉底层特征,如颜色、亮度、方向、纹理和边缘特征;加权融合成计算显著图;与眼动仪测得的眼动显著图进行相关系数比较,以确定各视觉底层特征的最佳权重。再根据权重的大小为不同类型的图像选择合适的图像增强方法。结果采用基于视觉底层特征的图像增强方法,其增强后的效果更符合人眼视觉感知。结论针对不同类型的图像需要充分考虑各视觉底层特征的权重大小,使其能真实反映视觉感兴趣区域,以达到提高图像增强后的视觉感知一致性。 相似文献
11.
A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t‐test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity. 相似文献
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R. Anitha D. Siva Sundhara Raja 《International journal of imaging systems and technology》2017,27(4):354-360
The abnormal development of cells in brain leads to the formation of tumors in brain. In this article, image fusion based brain tumor detection and segmentation methodology is proposed using convolutional neural networks (CNN). This proposed methodology consists of image fusion, feature extraction, classification, and segmentation. Discrete wavelet transform (DWT) is used for image fusion and enhanced brain image is obtained by fusing the coefficients of the DWT transform. Further, Grey Level Co‐occurrence Matrix features are extracted and fed to the CNN classifier for glioma image classifications. Then, morphological operations with closing and opening functions are used to segment the tumor region in classified glioma brain image. 相似文献
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Bharath Subramani Magudeeswaran Veluchamy 《International journal of imaging systems and technology》2018,28(3):217-222
In this article, fuzzy logic based adaptive histogram equalization (AHE) is proposed to enhance the contrast of MRI brain image. Medical image plays an important role in monitoring patient's health condition and giving an effective diagnostic. Mostly, medical images suffer from different problems such as poor contrast and noise. So it is necessary to enhance the contrast and to remove the noise in order to improve the quality of a various medical images such as CT, X‐ray, MRI, and MAMOGRAM images. Fuzzy logic is a useful tool for handling the ambiguity or uncertainty. Brightness Preserving Adaptive Fuzzy Histogram Equalization technique is proposed to improve the contrast of MRI brain images by preserving brightness. Proposed method comprises of two stages. First, fuzzy logic is applied to an input image and then it's output is given to AHE technique. This process not only preserves the mean brightness and but also improves the contrast of an image. A huge number of highly MRI brain images are taken in the proposed method. Performance of the proposed method is compared with existing methods using the parameters namely entropy, feature similarity index, and contrast improvement index and the experimental results show that the proposed method overwhelms the previous existing methods. 相似文献
15.
大部分注意力机制虽然能增强图像特征,但没有考虑局部特征的关联性影响特征整体的问题.针对以上问题,本文提出局部注意力引导下的全局池化残差分类网络(MSLENet).MSLENet的基线网络为ResNet34,首先改变首层结构,保留图像重要信息;其次提出多分割局部增强注意力机制(MSLE)模块,MSLE模块将图像整体分割成多个小图像,增强每个小图像的局部特征,通过特征组交互的方式将局部重要特征引导到全局特征中;最后提出池化残差(PR)模块来处理ResNet残差结构丢失信息的问题,提高各层之间的信息利用率.实验结果表明,MSLENet通过增强局部特征的关联性,在多个数据集上均有良好的效果,有效地提高了网络的表达能力. 相似文献
16.
目的依据压缩感知理论具有很好的计算保密性,提出一种基于NSCT和压缩感知的数字图像水印算法,以解决图像的鲁棒性、不可感知性及保密性之间矛盾。方法首先将水印图像分块压缩感知获得测量值,然后再将载体图像NSCT分解,对低频分量Fibonacci置乱后非重叠分块,对每块进行LU分解、奇异值分解,将每个分块的水印测量值按不同的嵌入强度对应嵌入载体奇异值矩阵中,经过一系列逆变换得到含水印图像。结果该算法在水印的嵌入和提取仿真实验结果中峰值信噪比大于40 d B,重建的水印图像与原图像相似度极高,且能抵抗剪切、高斯噪声、椒盐噪声、高斯低通滤波和JPEG压缩等类的攻击。结论该算法既具有很好的鲁棒性又兼有较强的不可见性,具有切实的可行性。 相似文献
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Abstract In recent years, Active Contour Models (ACMs) have become powerful tools for object detection and image segmentation in computer vision and image processing applications. This paper presents a new energy function in parametric active contour models for object detection and image segmentation. In the proposed method, a new pressure energy called “texture pressure energy” is added to the energy function of the parametric active contour model to detect and segment a textured object against a textured background. In this scheme, the texture features of the contour are calculated by a moment based method. Then by comparing these features with texture features of the object, the contour curve is expanded or contracted in order to be adapted to the object boundaries. Experimental results show that the proposed method has more efficient and accurate segmenting functionality than the traditional method when both object and background have texture properties. 相似文献
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Husam Yahya Dumitru Baleanu Rabha W. Ibrahim Nadia M.G. Al-Saidi 《计算机、材料和连续体（英文）》2023,74(2):2531-2539
The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis. The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability. The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety of MRI scan datasets of variable quality. For MRI scans, the BRISQUE “blind/referenceless image spatial quality evaluator” and the NIQE “natural image quality evaluator” measures were 39.38 and 3.58, respectively. The proposed image enhancement model, according to the data, produces the best image quality ratings, and it may be able to aid medical experts in the diagnosis process. The experimental results were achieved using a publicly available collection of MRI scans. 相似文献
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Hamid A. Jalab Rabha W. Ibrahim Ali M. Hasan Faten Khalid Karim Ala’a R. Al-Shamasneh Dumitru Baleanu 《计算机、材料和连续体（英文）》2021,68(2):1467-1483
The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’ probability. Initially, the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image. Next, the contrast of the image is then adjusted to enhance the regions with low visibility. Finally, the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image. Tests were conducted on brain MRI, lungs CT, and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality. The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores. Overall, this model improves the details of brain MRI, lungs CT, and kidney MRI scans, and could therefore potentially help the medical staff during the diagnosis process. 相似文献