Hierarchical Clustering-based Segmentation (HCS)

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Improving medical image perception by hierarchical clustering based segmentation

Improving medical image perception by hierarchical clustering based segmentation

In the hierarchical clustering based segmentation (HCS) an image is partitioned into its constituent regions at hierarchical levels of allowable dissimilarity between its different regions. At any particular level in the hierarchy, the segmentation process clusters together all the pixels and/or regions that have dissimilarity among them less than or equal to the dissimilarity allowed for that level. It should be noted that HCS, unlike other segmentation methods [4], is not an iterative optimization process. Instead at each level HCS yield an optimized segmentation output related to the dissimilarity allowed for that level. The algorithmic diagram, shown in Fig. 1, illustrates the overall operation of HCS [3]. The HCS process yields a merge tree. Based on the merge tree a GUI can display the hierarchy of merges.

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Hierarchical clustering based segmentation (HCS) aided interpretation of the DCE MR Images of the Prostate

Hierarchical clustering based segmentation (HCS) aided interpretation of the DCE MR Images of the Prostate

The demonstrated HCS process based CAM system aids the user to derive the maximum benefit from the computational capability (perception) of the machine and limits the machine's interpretive function. This complementary synthesis of both computer and human strengths enables aids the user to objectively choose the ROI having pixels of similar tissue type, involves the user in the diagnosis process by plotting the TIC for those HCS process segmented regions of interest identified, and the parametric image of the TIC aids the user to visualize the parametric details of the TIC like Time-To-Peak (TTP) and slope of the tail of the TIC. This will aid the user to make a more informed decision to accept or reject the machine's classification which was based on the actual values of those parameters. As a result the HCS based CAM system might improve overall accuracy. For further validation more exhaustive testing of this CAM software is in progress with radiologists and other medical imaging professionals.

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Boundary Extraction in Images Using Hierarchical Clustering based Segmentation

Boundary Extraction in Images Using Hierarchical Clustering based Segmentation

The corresponding scores for other segmentation algorithms ranges from 0.92 [13] to 0.76 [14]. (See [12]). The algorithm which has got the highest score of 0.92 is supervised [13] while HCS process is unsupervised. Moreover the human subjects' boundaries, with which the algorithms' output is compared, outline only the major structures in the scene. For example none of the human subjects has outlined the border of the bird's legs. In contrast the HCS process has not only outlined the legs of the bird but has also outlined the subtle difference in the chest plumage of the bird (Figure 5). This outlining of subtle differences within an otherwise a homogeneous region is found very useful in highlighting dissimilarities in diagnostic images. Because tissue abnormalities, in medical images are indicated, by part of the image being dissimilar from other homogeneous areas representing healthy parts.

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Hierarchical clustering based segmentation (HCS) aided diagstic image interpretation monitoring

Hierarchical clustering based segmentation (HCS) aided diagstic image interpretation monitoring

The Hierarchical Segmentation Software (HSEG) — developed by NASA's Goddard Space Flight Center Computer Engineer James C. Tilton, [Tilton, 2000], [Tilton, 2003] forms the core of a commercial medical segmentation program MED-SEG. MED-SEG is developed by Barton Medical Imaging Inc. a USA based company [http://www.bartron.ws/]. The FDA recently cleared the system to be used by trained professionals to process images. These images can be used in radiologist's reports and communications as well as other uses, but the processed images should not be used for primary image diagnosis. In the future, Dr. Molly Brewer, a professor with the Division of Gynecologic Oncology, University of Connecticut Health Center, would like to do clinical trials with the MED-SEG system. The goal, she said, would be improving mammography as a diagnostic tool for detecting breast cancer. "The MED-SEG processes the image allowing a doctor to see a lot more detail in a more quantitative way." (Figure 2.4). [http://www.nasa.gov/topics/nasalife/features/medical-imagerv.html]

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Highlighting dissimilarity in medical images using hierarchical clustering based segmentation (HCS)

Highlighting dissimilarity in medical images using hierarchical clustering based segmentation (HCS)

Tissue abnormality in a medical image is usually related to a dissimilar part of an otherwise homogeneous image. The dissimilarity may be subtle or strong depending on the medical modality and the type of abnormal tissue. Dissimilarity within an otherwise homogeneous area of an image may not always be due to tissue abnormality. It might be due to image noise or due to variability within the same tissue type. Given this situation it is almost impossible to design and implement a generic segmentation process that will consistently give a single appropriate solution under all conditions. Hence a dissimilarity highlighting process that yields a hierarchy of segmentation results is more useful. This would benefit from high level human interaction to select the appropriate image segmentation for a particular application, because one of the capabilities of the human vision process when visualising images is its ability to visualise them at different levels of details.

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An Image Segmentation comparison approach for Lesion Detection and Area calculation in Mangoes

An Image Segmentation comparison approach for Lesion Detection and Area calculation in Mangoes

Segmentation is the process subdivided into its constituent objects or groups. The objects once identified are analysed or subdivided to extract the information for high level machine vision activities. For any automated system various processes are required to segment an image into desired level. Such as in the automated inspection of electronic assemblies interest lies in analysing the images with the objective of determining the presence or absence of specific anomalies like missing components or broken connection paths therefore the segmentation stops when the objects or region of interest in an application has been identified. Therefore the segmentation termination depends on the type of application. It is one of the important tasks in machine vision applications and a quite difficult task in analysis applications. The success of machine vision and analysis applications depends on the success of segmentation. Therefore every image processing system should have robust image segmentation.

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Hierarchical Cluster Analysis to Aid Diagnostic Image Data
Visualization of MS and Other Medical Imaging Modalities

Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities

Border pixel reclassification is another unique feature of the HCS process (Fig. 7). Border pixel reclassification is considered only for those pixels on the boundary of the clusters which had been merged with other clusters. These boundary pixels are removed one at a time from their original clusters. The pixel removed is considered as a region of its own and the similarity between the one pixel region and the regions bordering it (which include the original cluster to which it belonged) is found and the single pixel region merged with the most similar bordering region. Border pixel reclassification aides in overrid- ing local inhomogeneity while clustering similar pixels/regions. The positive effect of border pixel re-classification can be visual- ized in Fig. 34. It can be seen from the border outlined images the HCS process with border pixel reclassification (top row) achieves far better results in delineating the different regions of an ill-defined abnormality presented in a X-ray mammogram (middle row) when compared to the segmentation without border-pixel-re-classification (bottom row) (Fig. 34).

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A Comparative Study of Brain Tumour Detection Using K- Harmonic Means, Expectation Maximization and Hierarchical Clustering Algorithms

A Comparative Study of Brain Tumour Detection Using K- Harmonic Means, Expectation Maximization and Hierarchical Clustering Algorithms

A new system that can be used as a second decision for the surgeons and radiologists is proposed. In this system brain tumours have been segmented with the help of three methods. The accuracy for Hierarchical clustering was high compared to the other methods and also the segmentation was intended. Thus tumour segmentation has a promising future as the universal segmentation algorithm and has become the focus of contemporary research. In spite of several decades of research up to now to the knowledge of authors, there is no universally accepted method for image segmentation, as the result of image segmentation is affected by lots of factors, such as: homogeneity of tumour’s, spatial characteristics of the image continuity, texture, image content. The study has focused on segmentation by k harmonic means; expectation maximization and hierarchical clustering based detection techniques and has made its observations. Thus there is hierarchical clustering method for accuracy which can be considered good for all type of images, and other methods equally good for time and iterations.

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Implementation of Hierarchical Clustering with Multiviewpoint-Based Similarity Measure

Implementation of Hierarchical Clustering with Multiviewpoint-Based Similarity Measure

Abstract— Clustering is one of the most important data mining or text mining algorithm that is used to group similar objects together. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the correctness of the similarity measure between the data in which the similarity can be computed. This paper focus on implementation of Agglomerative hierarchical clustering with Multiviewpoint based similarity measure for effective document clustering. The experiment is conducted over sixteen text documents and performance of the proposed model is analysed and compared to existing standard clustering method with MVS. The experiment results clearly shows that the proposed model Hierarchical Agglomerative Clustering with Multiview Point based Similarity Measure perform quite well.

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SEGMENTATION OF BLOOD VESSELS USING IMPROVED LINE DETECTION AND ENTROPY BASED 
THRESHOLDING

SEGMENTATION OF BLOOD VESSELS USING IMPROVED LINE DETECTION AND ENTROPY BASED THRESHOLDING

Sabino and Costa [13] used the Green channel of the RGB model to segment WBC. On the other hand, Westpfalz applied HSI color model to separate WBC from background and de-cluster the clustered WBC [12]. Yang, Foran and Meer [14] improved the algorithm of IGDS to better segment WBC from other BCs presented in the smear image. They used LUV color model, color gradient and used “least square estimation algorithm” along with GVF snake algorithm. Sinha and Ramakrishnan [15] used color used HIS equivalent of the WBC image, K-Means clustering followed by EM-algorithm to segment WBC along with the cytoplasm nucleus. Ongun et al. [16] used fuzzy patch labeling to segment WBC from other blood elements.

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Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data

Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data

Bioinformatics is a data intensive field of research and development. DNA microarray used to better understand form of saccharomyces cerevisiae disease such as cancer. Microarray allows us to diagnose and treat patients more successfully. Statistical method devoted to detection in DNA from microarray data, the inherent challenges in data quality associated with most filter techniques remains a challenging problem in microarray association studies. Applying methods of simulation studies and a genome-wide association microarray study in saccharomyces cerevisiae, that find current approach significantly improve DNA microarray cell reduces the yeast value rates and false positive genes variation. Clustering is the one of the main techniques for data mining. Microarray is the evolutionary history for a set of evolutionary related genes expression data. There are number of different distance based methods of which two are dealt with here: Euclidean method and Manhattan method.. A method for construction of distance based gens expression using clustering is proposed and implemented on different saccharomyces cerevisiae samples. Evolutionary distances between two or more genes are calculated using p-distance method. Multiple samples are applied on different datasets. Hierarchical clustering and k-mean clustering are constructed for different datasets from available data using both the distance based methods. Then, final cluster is constructed using these closely related filter dataset.

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Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering

Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering

As explained in [27], comparative assessment of segmenta- tion algorithms is often based upon subjective judgement, which is qualitative and time consuming. Although several measures can be applied in the presence of a ground-truth mask, the generation of ground truth requires significant effort and is often limited to foreground-background type of object segmentation. Selection of a conventional ground truth for multilevel object extraction algorithms may not be possible. For instance, what should be assigned as ground truth for 3-object level for Children sequence: two boys and the background, or one boy, ball and background, or some other possible combination? Should two boys constitute a single object, or should they be considered as separate en- tities? For two-object case, we hand segmented foreground object using the AMOS method since it is semiautomatic. However, we stopped the tracker whenever it makes an error and corrected the object boundary accordingly. We observed that even for the experienced users and careful initialization, the generation of ground truth is very exhausting and it takes more than 20 seconds for a single frame on average.

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HIERARCHICAL CLUSTERING BASED MULTI-DIMENSIONAL POLYGON REDUCTION ALGORITHM FOR LARGE SPATIAL DATA

HIERARCHICAL CLUSTERING BASED MULTI-DIMENSIONAL POLYGON REDUCTION ALGORITHM FOR LARGE SPATIAL DATA

In COD-CLARANS [13], the authors have represented obstacles through visibility graph and thus computed the obstructed distance between data objects. Also, it detects mostly spherical shaped clusters and depends on user- defined parameters. AutoClust+ [14], which is a graph-based approach, the dataset is modeled through Delaunay structure. DBCluC [15], an extended form of DBSCAN [17], models obstacles using polygons and these polygons are reduced to minimum number of line segments called as obstruction lines that does not compromise with the visibility space. The Fig. 2 shows the obstacle modeling in DBCluc [15]. This approach handles facilitators also, using the concept of entry and exit points.

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A Survey on Clustering Based Image Segmentation

A Survey on Clustering Based Image Segmentation

The recursive Ncut partition is essentially a hierarchical divisive clustering process that produces a tree [37]. For example, Figure 2 shows a tree generated by four Recursive Ncuts. The first Ncut divides V into C1 and C2. Since C2 is larger than C1, the second Ncut partitions C2 into C3 and C4.Next, C3 is further divided because it is larger than C1 and C4. The fourth Ncut is applied to C1, and gives the final five clusters (or leaves): C4, C5, C6, C7 and C8.The above example suggest trees as a natural organization of clusters [35]. Nonetheless, the tree organization here may mislead a user because there is no guarantee of any correspondence between the tree and the semantic structure of images. Furthermore, organizing image clusters into a tree structure will significantly complicate the user interface.

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Comparative Study of Weighted Clustering Algorithms for Mobile Ad Hoc Networks

Comparative Study of Weighted Clustering Algorithms for Mobile Ad Hoc Networks

The weight-based distributed clustering algorithm (WCA) [4] takes into consideration, the ideal degree, transmission power, mobility, and battery power of mobile nodes. Depending on specific applications, any or all of these parameters can be used in the metric to elect the CHs. This is based on fully distributed approach, where all the nodes in the mobile network share the same responsibility and act as CHs. The time required to identify the CHs depends on the diameter of the underlying network graph. This method keeps a predefined threshold value for no. of mobile nodes in a cluster. The CH election procedure is invoked only on-demand thus reduces routing control overhead.

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Octree and Clustering Based Hierarchical Ensemble Visualization.

Octree and Clustering Based Hierarchical Ensemble Visualization.

Member segmentation combines similar shapes in a local region in time, discovers smooth or rapid shape changes in ensemble members, and captures time-steps with important shape changes. It lays a foundation for abstract member visualization, enabling scientists to visualize the abstract shape changes summarized by sequences of member segments. We apply an octree integration based technique to efficiently compare segments. A cluster of segments combines similar shapes in different members and at different time-steps. In time-step ensemble analysis, a time-step shape cluster captures similar shapes across members at a given time-step, enabling analysis and visualization of hierarchical static inter-member relationships. Generating segment dissimilarity and member dissimilarity matrices at every time-step is expensive. To improve efficiency, our system can generate pre-calculated segmentation result and dissimilarity matrices. Pattern mining enables scientists to discover time-regions where shape changes are similar over multiple members. Patterns resulting from member cluster participation sequences capture common shape changes at a more abstract view, considering possible shifting and distortion in the time dimension. The corresponding pattern visualization summarizes important shape changes based on clusters of member segments. Patterns resulting from time-step shape cluster participation sequences capture common shape changes at a more detailed level, based on members directly aligned at each time-step. The corresponding pattern visualization encodes time-step by time-step comparisons of all occurrences of the patterns.

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Data Mining and Clustering Techniques

Data Mining and Clustering Techniques

the items retrieved to be clustered and used to create a visual (e.g., graphical) representation of the clusters and their topics. This allows a user to navigate between topics, potentially showing topics the user had not considered. The topics are not defined by the query but by the text of the items retrieved. When items in the database have been clustered, it is possible to retrieve all of the items in a cluster, even if the search statement did not identify them. When the user retrieves a strongly relevant item, the user can look at other items like it without issuing another search. When relevant items are used to create a new query (i.e., relevance feedback), the retrieved hits are similar to what might be produced by a clustering algorithm.

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IMAGE MINING AND CLUSTERING BASED SEGMENTATION

IMAGE MINING AND CLUSTERING BASED SEGMENTATION

Available Online at www.ijpret.com 1734 local thresholding. Global thresholding methods can fail when the background illumination is uneven so to compensate for this uneven illumination we can use multiple thresholds and the threshold selection is typically done interactively. These methods are popular because of their simplification and efficiency. The problems that arise in this kind of method is that basic histogram based thresholding algorithm do not process those images which have histograms that are unimodal when the target segment is much smaller than the background area[11]. Edge Detection Based Methods:

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Study on Clustering of Data

Study on Clustering of Data

Clustering is unsupervised learning. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. This means that if any program or system starts to learn from a given set of data and apply it on a new data, and its performance increases, then that can be defines as learning. There are two types of learning- Supervised and Unsupervised learning.

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Keep Meeting Summaries on Topic: Abstractive Multi Modal Meeting Summarization

Keep Meeting Summaries on Topic: Abstractive Multi Modal Meeting Summarization

2016a; Shang et al., 2018). However, extractive summaries are often not natural and coherent with limited content coverage. Recently the neural nat- ural language generation models boost the perfor- mance of abstractive summarization (Luong et al., 2015; Rush et al., 2015; See et al., 2017), but they are often unable to focus on topic words. In- spired by utterance clustering in extractive meth- ods (Shang et al., 2018), we propose a hierar- chical attention based on topic segmentation (Li et al., 2018). Moreover, our hierarchical attention is multi-modal to narrow down the focus by cap- turing participant interactions. Multi-modal fea- tures from human annotations have been proven effective at improving summarization, such as di- alogue act (Goo and Chen, 2018). Instead of using human annotations, our approach utilizes a simply detectable multi-modal feature VFOA.

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