The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Microscopy cell image analysis is a fundamental tool for biological research. This analysis is used in studies of different aspects of cell cultures. The main challenges in segmenting nuclei in histometry are due to the fact that the specimen is a 2-D section of a 3-D tissue sample. The 2-D sectioning can result in partially imaged nuclei, sectioning of nuclei at odd angles, and damage due to the...
Image segmentation is one of the most important research areas in image processing and computer vision, and is a key step in image processing and image analysis. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. Based on the measure of medium truth degree, this paper presents a novel image segmentation method by introducing the distance...
With the development of the Broadcasting and Video network, the Monitoring System on Digital Video Broadcasting is becoming more and more important. Image recognition technology is widely applied to detect the degraded video in the television observation system. Mosaic block easily occurs in the TV signals, which will degrade the video quality. The conventional mosaic detection algorithm can't distinguish...
Everyday medical is capturing thousands of images which need to be classified in a proper way. In this paper, we address the problem of replacing the existing images with the captured one. We provide a new solution by storing only the nonexisting part of the image. Though medical images have been classified in past by using various techniques, the researchers are always finding alternative strategies...
An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive...
This paper proposes a general benchmark for interactive segmentation algorithms. The main contribution can be summarized as follows: (I) A new dataset of fifty images is released. These images are categorized into five groups: animal, artifact, human, building and plant. They cover several major challenges for the interactive image segmentation task, including fuzzy boundary, complex texture, cluttered...
In this paper, the automatic segmentation of Osteosar-coma in MRI images is formed as a clustering problem. Subsequently, a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means (FCM) called DCHS is proposed to automatically segment the Osteosarcoma MRI images in an intelligent manner. The concept of variable length in each harmony memory vector is applied...
This paper proposes a novel algorithm for simultaneous estimation of the bias field and segmentation of tissues for magnetic resonance images. The algorithm formulated by modifying the objective function in the fuzzy C-means algorithm to include a bias field which is modeled as a linear combination of a set of basis functions. Bias field estimation and image segmentation are simultaneously achieved...
Stereo matching techniques usually match segments or blocks of pixels. This paper proposes to match segments defined as fuzzy sets of pixels. The proposed matching method is applicable to various techniques of stereo matching as well as to different measures of differences between pixels. In the paper, embedment of this approach into the state-of-the-art depth estimation software is described. Obtained...
In this paper, a novel approach to MRI Brain Image segmentation based on the Hybrid Parallel Ant Colony Optimization (HPACO) with Fuzzy C-Means (FCM) Algorithm have been used to find out the optimum label that minimizes the Maximizing a Posterior (MAP) estimate to segment the image. There are M colonies, M-1 colonies treated as slaves and one colony for master. Each colonies visit all the pixels with...
Shadows arise when objects completely or somewhat occlude direct light from a source of illumination. This method is based on Tri color attenuation model (TAM). The proposed approach is based on shadow properties and exploits color information. Shadow is treated as a extraordinary kind of image degradation. Shadow area generally has lower brightness than non-shadow region. Based on TAM a shadow detection...
ADS40 images with High spatial resolution have more spatial characteristics as well as spectral characteristics than low-resolution data. In this paper,an object-oriented classification method based on multi-scale segmentation is introduced to classify ADS40 image of Taiyuan city. Firstly,a multi-scale segmentation algorithm is applied to get objects.Then,the features of objects,such as spectral,...
Change detection in images of a given scene acquired at different times is one of the most interesting topics of image processing. A new change detection method based on 2-D fuzzy entropies is proposed in this paper to detect change area of the difference image. First, the best segmentation direction of 2-D histogram formed by pixel gray levels and the local average gray levels is found by using Fisher...
Abdominal aortic aneurysm (AAA) is a cardiovascular disease which mostly appears in elderly people. Due to the weakening of aortic wall, a rupture occurs in the most inner layer of aorta and a thrombus is generated. If the diameter exceeds greater than 5.5 cm, a treatment strategy is required. As a result, CT imaging is utilized to screen and evaluate thrombus parameters for treatment. Exploitation...
In this paper, we manage to use the clustering method realize sonar image segmentation. A particle swarm optimization (PSO) based FCM algorithm (PSO-FCM) is proposed which PSO incorporate with Fuzzy Clustering Method(FCM). The algorithm takes the clustering result of PSO as the initialization of the FCM, and uses fuzzy measures and fuzzy integrals to express the adapt function. At last, the algorithm...
Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher value...
Clustering or data grouping is a key initial procedure in image processing. In present scenario the size of database of companies has increased dramatically, these databases contain large amount of text, image. They need to mine these huge databases and make accurate decisions in short durations in order to gain marketing advantage. As image is a collection of number of pixels. It is difficult to...
This paper presents the results of some partitional clustering algorithms applied to the segmentation of color images in the RGB space. As more information is involved in the algorithm, and the distance measure is more flexible, the better the results. The selected algorithms for this work are the K-means, the FCM, the GK-B, and the GKPFCM. The GKPFCM gives the better results when all the algorithms...
Aiming at threshold uncertainty caused by fuzziness of image for image segmentation, an adaptive thresholding method for gray-level image segmentation using type-II fuzzy sets is proposed . Fuzzy index is got using type-II fuzzy sets technique, that can overcome effectively the uncertainty, histogram peaks value of an image can be find out automatically based on maximum Mahalanobis distance and minimum...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.