Sodar image segmentation by fuzzy c-means

Mukherjee, Debargha; Pal, Pinakpani; Das, J.

doi:10.1016/s0165-1684(96)00115-6

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…According to [32], [33], the FCM algorithm assigns pixels to each category by using fuzzy memberships. Let denote an image with pixels to be partitioned into clusters.…”

Section: ) Characteristics Of a Hole Imagementioning

confidence: 99%

“…The cost function satisfies a least-squared error criterion (1) where represents the membership of pixel in the cluster; , whose number of components depends on the number of feature vectors (make reference to [32]), is the cluster center of class of fuzzy c-partitions, also called seed point for the particular class. The results of the calculated error or cost function are updated in every iteration according to and the distance .…”

Section: ) Characteristics Of a Hole Imagementioning

confidence: 99%

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Local Computed Tomography Using a THz Quantum Cascade Laser

Yin

Zeitler

et al. 2010

IEEE Sensors J.

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Abstract-This paper investigates local reconstruction techniques for extracting region-of-interest (ROI) from a 3-D terahertz imaging setup using a quantum cascade laser (QCL). The advantage of local reconstruction is the reduction in the required measurement time. Difficulties with limited projection angles and image noise make the development of accurate reconstruction algorithms particularly challenging. In this paper, both wavelet-based and traditional filtered back projection (FBP) techniques are investigated. Segmentation algorithms are applied to reconstructed images with low contrast and the resultant segments are compared with a known ground truth to explore the ability of a QCL to image target objects with complex contours. In our experiments, a polystyrene object with a hole drilled inside is used as the imaging target. The region of interest is adjusted through changing the size of the exposure regions. It is found that 3-D local reconstruction of the interior hole suffers from shape distortion, since scattering caused by the target's exterior contours introduces errors in the measured optical parameters of the object. It is found that wavelet-based local computed tomography for terahertz image reconstruction results in lower misclassification of pixels than traditional FBP algorithms.Index Terms-Computed tomography, discrete wavelet transforms (DWT), filtered back projection, fuzzy c-means threshold, quantum cascade laser (QCL), terahertz.

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“…According to [32], [33], the FCM algorithm assigns pixels to each category by using fuzzy memberships. Let denote an image with pixels to be partitioned into clusters.…”

Section: ) Characteristics Of a Hole Imagementioning

confidence: 99%

Section: ) Characteristics Of a Hole Imagementioning

confidence: 99%

Local Computed Tomography Using a THz Quantum Cascade Laser

Yin

Zeitler

et al. 2010

IEEE Sensors J.

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“…After the prime classification, vehicle classification will be refined in Class one and Class two by means of the shape feature of wavelet fractal signatures. Since the FCM has been confirmed to be an effective data clustering algorithm [7], we apply the Fuzzy c-means Clustering Method to the vehicle shape feature data set. The conventional FCM method should be briefly introduced in the following.…”

Section: Segmentation Of the Vehicle Roimentioning

confidence: 99%

Robust Classification of Vehicle based on Fusion of TSRP and Wavelet Fractal Signature

Zhang

Liu

2008

2008 IEEE International Conference on Networking, Sensing and Control

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This paper presents a new vehicle's shape representation, which can describe its shape features. An algorithm called transformation-ring-projection (TRP), which is usually used in the recognition of characters in a binary image, is now applied to obtain multiple one-dimension patterns of vehicle shape. Firstly, in order to acquire high classification accuracy of vehicle types, we apply transformations-semi-ring-projection (TSRP) at eight central points which are distributed on the minimum ring of the vehicle region-of-interest (ROI) to traffic images and can obtain eight one-dimension patterns. Secondly, we calculate the fractal signatures in discrete wavelet transformation (DWT) domain of four one-dimension patterns. Thirdly, applying the MFC algorithm to process this kind of shape feature vector data set and generating several clusters. Finally, vehicles can be classified by shape feature matching system. Experiments results demonstrate the effectiveness and robustness of the proposed vehicle classification scheme.

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“…Fuzzy c-means (FCM), an unsupervised clustering algorithm, has been applied successfully to a number of problems involving feature analysis, clustering and classifier design in fields such as agricultural engineering, astronomy, chemistry, geology, image analysis, medical diagnosis, shape analysis, target recognition [18] and image segmentation [2], [10], [13], [15], [19]- [23]. Although the original algorithm dates back to 1973 [24], [25], derivatives have been described with modified definitions for the norm and prototypes for the cluster centers [26]- [28].…”

Section: B Fuzzy C-means Clusteringmentioning

confidence: 99%

A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering

Rezaee

Zwet

Lelieveldt

et al. 2000

IEEE Trans. on Image Process.

140

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In this paper, an unsupervised image segmentation technique is presented, which combines pyramidal image segmentation with the fuzzy c-means clustering algorithm. Each layer of the pyramid is split into a number of regions by a root labeling technique, and then fuzzy c-means is used to merge the regions of the layer with the highest image resolution. A cluster validity functional is used to find the optimal number of objects automatically. Segmentation of a number of synthetic as well as clinical images is illustrated and two fully automatic segmentation approaches are evaluated, which determine the left ventricular volume (LV) in 140 cardiovascular magnetic resonance (MR) images. First fuzzy c-means is applied without pyramids. In the second approach the regions generated by pyramidal segmentation are merged by fuzzy c-means. The correlation coefficients of manually and automatically defined LV lumen of all 140 and 20 end-diastolic images were equal to 0.86 and 0.79, respectively, when images were segmented with fuzzy c-means alone. These coefficients increased to 0.90 and 0.93 when the pyramidal segmentation was combined with fuzzy c-means. This method can be applied to any dimensional representation and at any resolution level of an image series. The evaluation study shows good performance in detecting LV lumen in MR images.

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Sodar image segmentation by fuzzy c-means

Cited by 14 publications

References 3 publications

Local Computed Tomography Using a THz Quantum Cascade Laser

Local Computed Tomography Using a THz Quantum Cascade Laser

Robust Classification of Vehicle based on Fusion of TSRP and Wavelet Fractal Signature

A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering

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