Pattern Recognition with Fuzzy Objective Function Algorithms

Bezdek, James C.

doi:10.1007/978-1-4757-0450-1

Cited by 12,647 publications

(7,161 citation statements)

References 36 publications

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“…2a). In a and c the trials are ordered as they are recorded, whereas in b and d they are reordered using fuzzy K-means clustering 139 to bring similar trials close to each other 18 . Spike patterns are operationally defined as groups of trials that are more similar to each other than to the other trials.…”

Section: Feedforward Informationmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

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A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.Reliability and precision are two different quantities. When you make an appointment with your friend, she can either keep the appointment or not show up at all. If she does show up, she might or might not be on time. The former uncertainty is related to reliability, whereas the latter is related to precision. When the same stimulus waveform is repeatedly injected at the soma of a neuron in vitro (FIG. 1a), a similar spike train is obtained on each trial 1,2 (FIG. 1b). When approximately the same number of spikes occur on each trial the neuron is said to be reliable, whereas when the spikes occur almost at the same time across trials it is said to be precise (FIG. 1c). For a single neuron, the potential information content of precise and reliable spike times is many times larger than that which is contained in the firing rate, which is averaged across a typical interval of a hundred milliseconds [3][4][5][6] . The information contained in spike timing is available immediately, rather than after an averaging period. Furthermore, the timing of patterns of spikes can potentially transmit even more information than the timing of the individual constituent spikes 3,7 . The potential relevance of spike patterns becomes apparent when we consider neurons at the population level: when a group of similar neurons (a 'pool') produces precise and reliable spike trains, the neurons they project to receive volleys of synchronous spikes 8,9 . This opens up the possibility of communicating between different cortical areas through synchronous spike volleys.In contrast to the in vitro situation described above, in the intact cortex most excitatory synaptic inputs arrive at the dendrites rather than at the soma (FIG. 1d), and synaptic transmission is typically unreliable [10][11][12][13] . Furthermore, most of these dendritic inputs are not directly related to ongoing sensory stimulation; rather, they reflect spatiotemporally structured internal activity. Therefore, when the same stimulus is presented repeatedly, the resulting spike trains are usually neither precise nor reliable when they are aligned to the stimulus onset 6 . Instead, HHMI Author ManuscriptHHMI Author Manuscript HHMI Author Manuscript neural activity in vivo might be dominated by internally generated complex reverberations or rhythmic oscillations, and precise and reliable sp...

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Section: Feedforward Informationmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

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“…In addition, more complicated methods such as those described in 27,11 would be helpful in increasing the computational efficiency. Further performance improvement could be obtained by considering the grand mean and cluster scatter matrices within and between clusters in the unsupervised clustering 45,19,8,9,12,5,6,28,29,39,52.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore b k can be written as (7) with and where m is the number of basis functions in Φ. The whole bias field can be expressed as (8) In practice, the approximation of the bias field is over the 3D space, and the spatial relation has to be included into the expansion of the smooth basis functions.…”

Section: A Multi-spectral Adaptive Fcmmentioning

confidence: 99%

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

He¹,

Sajja²,

Datta³

et al. 2008

Ann Biomed Eng

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Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy cmeans (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

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“…In fuzzy C-means (FCM) algorithm [33], each instance has a degree of belonging to clusters, rather than completely belonging to just one cluster. It iteratively classifies the data into optimal c partitions.…”

Section: And Fuzzy C-meansmentioning

confidence: 99%

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

et al. 2012

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Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p<0.005).

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Pattern Recognition with Fuzzy Objective Function Algorithms

Cited by 12,647 publications

References 36 publications

Regulation of spike timing in visual cortical circuits

Regulation of spike timing in visual cortical circuits

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

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