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- 实验方案
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Hybrid technique for the detection of suspicious lesions in digital mammograms
摘要： This paper presents an efficient system for the detection of suspicious lesions in mammograms. The proposed detection system consists of three steps. In the first step, an efficient pre-processing technique is developed using Top-Hat morphological filter and NL means filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM), gradient magnitude (GM), and intensity contrast (IC). Finally, computed threshold is used to extract the suspicious lesions in mammograms. The Free Response Operating Characteristics (FROC) curve is used to assess the performance of the proposed system. Proposed system achieved the sensitivity of 93.8% at the rate of 0.51 false positives per image.
关键词： breast cancer,segmentation,computer-aided diagnosis,fuzzy C-means,mammograms
更新于2025-09-23 15:22:29
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Mixed Pixel Decomposition Based on Extended Fuzzy Clustering for Single Spectral Value Remote Sensing Images
摘要： The presence of mixed pixels in remote sensing images is the major issue for accurate classification. In this paper, we have focused on two aspects of mixed pixel problem: firstly, to identify mixed pixels from an image and secondly to label them to their appropriate class. In phase I, extraction of mixed pixels has been performed from the RSI images-based super-pixel algorithm and RGB model by using fuzzy C-means (FCM). In phase II, the extracted mixed pixel from phase I has been decomposed to the appropriate class. This new proposed technique is the amalgamation of PSO-FCM (particle swarm optimization-fuzzy C-means) for clustering of mixed pixels and ANN-BPO (artificial neural network-biogeography-based particle swarm optimization) for the classification purpose. Experimental results reveal that the proposed method has improved the accuracy as compared to the existing techniques and succeeds in better classification of the remote sensing images.
关键词： Fuzzy C-means,BBO,Remote sensing images,Pure pixels,Mixed pixels,PSO,Neural network
更新于2025-09-23 15:22:29
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[IEEE 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Yassmine Hammamet, Tunisia (2018.3.19-2018.3.22)] 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Developing Modified Fuzzy C-Means Clustering Algorithm for Image Segmentation
摘要： Effective algorithm for segmenting image is important for images analysis and computer vision. Fuzzy c-means (FCM) is the mostly used methodology in image clustering. However, the results of the standard and the modified version FCM are not always satisfactory. This paper introduces a modification on spatial FCM considering the weighted fuzzy effect of neighboring pixels on the center of the cluster. So, the objective function in FCM algorithm is modified to minimize the intensity inhomogeneities by implicating the spatial information and the modified membership weighting. The advantages of the new FCM algorithm are: (a) produces homogeneous regions, (b) handles noisy spots, and (c) relatively less sensitive to noise. Experimental results on real images show that the algorithm is effective, efficient, and is relatively independent of the type of noise. Especially, it can process non-noisy and noisy images without knowing the type of the noise.
关键词： image processing,images segmentation,fuzzy c-means,image clustering
更新于2025-09-23 15:22:29
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GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy C-Mean Clustering
摘要： Change detection in remote sensing images turns out to play a significant role for the preceding years. Change detection in synthetic aperture radar (SAR) images comprises certain complications owing to the reality that it endures from the existence of the speckle noise. Hence, to overcome this limitation, this paper intends to develop an improved model for detecting the changes in SAR image. In this model, two SAR images captivated at varied times will be considered as the input for the change detection process. Initially, discrete wavelet transform (DWT) is employed for image fusion, where the coefficients are optimized using improved grey wolf optimization (GWO) called adaptive GWO (AGWO) algorithm. Finally, the fused images after inverse transform are clustered using fuzzy C-means (FCM) clustering technique and a similarity measure is performed among the segmented image and ground truth image. With the use of all these technologies, the proposed model is termed as adaptive grey wolf-based DWT with FCM (AGWDWT-FCM). The similarity measures analyze the relevant performance measures such as accuracy, specificity and F1 score. Moreover, the performance of the AGWDWT-FCM in change detection model is compared to other conventional models, and the improvement is noted.
关键词： Filter coefficient,Adaptive discrete wavelet transform,Grey wolf optimization,Synthetic aperture radar,Fuzzy C-means clustering
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Simple Fusion Approach of Chlorophyll Images and Sea Surface Temperature Images for Improving the Detection of Moroccan Coastal Upwelling
摘要： In order to improve the decision-making on the Moroccan upwelling region detection, we present in this paper a simple and reliable fusion approach. In this context, we started by applying Fuzzy C-means algorithm on each 46 Sea Surface Chlorophyll images and on each 46 Sea Surface Temperature images during the year of 2014. After that, we implement post classification fusion by using logical AND operator set to combine FCM result of the both types and consequently having single image more informative and suitable for visual perception. The oceanographer validation indicate that the proposed methodology detect automatically and effectively the different Moroccan coastal upwelling scenarios of our database.
关键词： Moroccan Coastal Upwelling,Fuzzy C-means,Sea Surface Temperature Image,Sea Surface Chlorophyll Image,Post Classification Fusion
更新于2025-09-23 15:21:01
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Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix
摘要： Mid-infrared diffuse reflectance spectroscopy was used to rapidly and nondestructively identify tea varieties together with the proposed possibilistic fuzzy c-means (PFCM) clustering with a fuzzy covariance matrix. The mid-infrared diffuse reflectance spectra of 96 tea samples with three different varieties (Emeishan Maofeng, Level 1, and Level 6 Leshan trimeresurus) were acquired using the FTIR-7600 infrared spectrometer. First, multiplicative scatter correction was implemented to pretreat the spectral data. Second, principal component analysis was employed to compress the mid-infrared diffuse reflectance spectral data after preprocessing. Third, linear discriminant analysis was utilized for extracting the identification information required by the fuzzy clustering algorithms. Ultimately, the fuzzy c-means (FCM) clustering, the allied fuzzy c-means (AFCM) clustering, the PFCM clustering, and the PFCM clustering with a fuzzy covariance matrix were used to cluster the processed spectral data, respectively. The highest identification accuracy of the PFCM clustering with a fuzzy covariance matrix reached at 100% compared with those of FCM (96.7%), AFCM (94.9%), PFCM (96.3%), and partial least squares discrimination analysis (PLS-DA) algorithm (33.3%). It is sufficiently demonstrated that the mid-infrared diffuse reflectance spectroscopy coupled with the PFCM clustering with a fuzzy covariance matrix was a valid method for identifying tea varieties.
关键词： possibilistic fuzzy c-means clustering,tea varieties,Mid-infrared diffuse reflectance spectroscopy,fuzzy covariance matrix,nondestructive detection
更新于2025-09-11 14:15:04
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Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information
摘要： Landcover classifications have large uncertainty related to the heterogeneity of similar objects and complex spatial correlations in satellite images, making it difficult to obtain ideal classification results using traditional classification methods. Therefore, to address the uncertainty in landcover classifications based on remotely sensed information, we propose a novel fuzzy c-means algorithm, which integrates adaptive interval-valued modelling and spatial information. It dynamically adjusts the interval width according to the fuzzy degree of the target membership without pre-setting any parameters, controls the fuzziness of the target, and mines the inherent distribution of the data. Furthermore, reliability-based spatial correlation modelling is used to describe the spatial relationship of the target and to improve both robustness and accuracy of the algorithm. Experimental data consisting of SPOT5 (10-m spatial resolution) or Thematic Mapper (30-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm markedly improved the ground-object separability. Moreover, it balanced improvement of pixel separability and suppression of heterogeneity of intra-class objects, producing more compact landcover areas and clearer boundaries between classes.
关键词： satellite images,spatial information,adaptive interval fuzzy c-means algorithm,Landcover classification
更新于2025-09-11 14:15:04
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[IEEE 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Image Segmentation Algorithm Based On Clustering
摘要： Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy C-means algorithm and mean-shift algorithm. The fuzzy C-means algorithm does not consider the spatial information of the image. Put forward an fuzzy C-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.
关键词： Fuzzy C-means algorithm,Clustering,Image segmentation,Mean shift algorithm
更新于2025-09-10 09:29:36
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[Lecture Notes in Electrical Engineering] Microelectronics, Electromagnetics and Telecommunications Volume 521 (Proceedings of the Fourth ICMEET 2018) || Extraction of Lesion and Tumor Region in Multi-modal Images Using Novel Self-organizing Map-Based Enhanced Fuzzy C-Means Clustering Algorithm
摘要： Analyzing the medical images and segmenting the same for detecting the tumor and lesion regions embedded within the images are quite a tedious process. On performing the task of tumor and lesion region detection, several intricacies arise and two of the major hindrances are time complexity and accuracy level sustainment. Resolving these two issues is the major concern of this paper and the authors have achieved it, which could be veri?ed from the ?gures of this paper. If the examination of the medical images obtained through modalities such as MRI and CT is clearly processed using an algorithm, preplanning of surgical procedures could be made with ease. The development of such an algorithm is focused by the authors, and the algorithm framed in this research ensemble the working of self-organizing map (SOM) and enhanced fuzzy C-means (EnFCM), and the authors have collectively named the algorithm as SOM-based EnFCM. The proposed algorithm has produced a high peak signal-to-noise ratio (PSNR) value of 60 dB and mean square error (MSE) of 0.06. The time required by the algorithm for processing 71 input slice images acquired through CT and MRI scans is around 6 s, and the overall accuracy exhibited by the algorithm is 48%. This has given a new and a dynamic approach, which could be greatly used by the radiologists in clinical practices. To contest and prove the ef?ciency of the SOM–EnFCM algorithm, the segmentation results of SOM and EnFCM algorithms while operating individually are compared.
关键词： Tumor identi?cation,Self-organized map algorithm,Enhanced fuzzy C-means algorithm,Tissue segmentation
更新于2025-09-09 09:28:46
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A Multi-Feature Fusion-Based Change Detection Method for Remote Sensing Images
摘要： An object-oriented change detection method for remote sensing images based on multiple features using a novel weighted fuzzy c-means (WFCM) method is presented. First, Gabor and Markov random ?eld textures are extracted and added to the original images. Second, objects are obtained by using a watershed segmentation algorithm to segment the images. Third, simple threshold technology is applied to produce the initial change detection results. Finally, re?ning is conducted using WFCM with different feature weights identi?ed by the Relief algorithm. Two satellite images are used to validate the proposed method. Experimental results show that the proposed method can reduce uncertainties involved in using a single feature or using equally weighted features, resulting in higher accuracy.
关键词： Feature weight,Multi-feature fusion,Fuzzy c-means,Object-oriented change detection
更新于2025-09-09 09:28:46