MRI Image Segmentation Using Unsupervised Clustering Techniques

Selvathi, D.; Arulmurgan, A.; Seivi, S. Thamarai; Alagappan, S.

doi:10.1109/iccima.2005.40

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…For example Lee and Street [2] have used neuro-fuzzy approach to segment humans in video sequences whereas Selvathi et al [3] combined a genetic algorithm and fuzzy c-means (FCM) clustering to the MRI segmentation problem. Spatial information can be included into such frameworks for increased accuracy.…”

Section: Introductionmentioning

confidence: 99%

Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions

Reilly

Kurugöllü

Miller

2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

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A change detection framework which fuses both spatial and temporal data using fuzzy if-then rules is presented. Temporal data is used on a per-pixel basis to monitor the sequence for changes by employing a fuzzy codebook model. Spatial data is gathered using a fuzzy multithresholding algorithm that decomposes the RGB color space into three color pair histograms. This system is found to be robust to noise and allows the algorithm to process successfully even when the underlying sequences result in under-segmentation of the spatial data.

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Section: Introductionmentioning

confidence: 99%

Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions

Reilly

Kurugöllü

Miller

2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

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show abstract

“…The system consists of four major components: image preprocessing, nuclei segmentation, nuclei tracking, and cell phase identification. Although a lot of previous work has focused on some subproblems such as segmentation and cell phase identification [2], [4], [5], it is difficult to consider the problems from a systematic view due to error propagation. On the other hand, from a systematic view, there is additional information available for improving the performance of the whole system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model

Zhou

Yan

et al. 2009

IEEE Trans. Inform. Technol. Biomed.

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Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.

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“…III. SEGMENTATION [17] In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels)as shown in (Figure 4).…”

mentioning

confidence: 99%

Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm

Abbas¹,

Rydh²

2012

IJIGSP

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In this paper, a adopted approach to fully automatic satellite image segmentation, called JSEG, "JPEG image segmentation" is presented. First colors in the image are quantized to represent differentiate regions in the image. Then image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values corresponding to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale Jimages. Experiments show that JSEG provides good segmentation and classification results on a variety of images.

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MRI Image Segmentation Using Unsupervised Clustering Techniques

Cited by 10 publications

References 7 publications

Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions

Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions

A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model

Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm

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