Jzau-Sheng Lin

The application of competitive Hopfield neural network to medical image segmentation

In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.

A monitoring system based on wireless sensor network and an SoC platform in precision agriculture

This paper proposed a field signals monitoring system with wireless sensor network (WSN) which integrates a System on a Chip (SoC) platform and Zigbee wireless network technologies in precision agriculture. The designed system was constituted by three parts which include field-environment signals sensing units, Zigbee transceiver module and web-site unit. Firstly we used acquisition sensors for field signals, an MCU as the front-end processing device, and several amplifier circuits to process and convert signals of field parameter into digital data. Secondly, Zigbee module was used to transmit digital data to the SoC platform with wireless manner. Finally, an SoC platform, as a Web server additionally, was used to process field signals. Then, we created a system in which field signal values are displayed on Web page or collected into control center in real-time through RJ-45 with the SoC platform. The experimental results show our proposed field-environment signals monitoring system is very feasible for future applications in precision agriculture.

Fuzzy Clustering Using A Compensated Fuzzy Hopfield Network

Hopfield neural networks are well known for cluster analysis with an unsupervised learning scheme. This class of networks is a set of heuristic procedures that suffers from several problems such as not guaranteed convergence and output depending on the sequence of input data. In this paper, a Compensated Fuzzy Hopfield Neural Network (CFHNN) is proposed which integrates a Compensated Fuzzy C-Means (CFCM) model into the learning scheme and updating strategies of the Hopfield neural network. The CFCM, modified from Penalized Fuzzy C-Means algorithm (PFCM), is embedded into a Hopfield net to avoid the NP-hard problem and to speed up the convergence rate for the clustering procedure. The proposed network also avoids determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn more rapidly and more effectively than FCM and PFCM. In experimental results, the CFHNN method shows promising results in comparison with FCM and PFCM methods.

Vector quantization in DCT domain using fuzzy possibilistic c-means based on penalized and compensated constraints

In this paper, fuzzy possibilistic c-means (FPCM) approach based on penalized and compensated constraints are proposed to vector quantization (VQ) in discrete cosine transform (DCT) for image compression. These approaches are named penalized fuzzy possibilistic c-means (PFPCM) and compensated fuzzy possibilistic c-means (CFPCM). The main purpose is to modify the FPCM strategy with penalized or compensated constraints so that the cluster centroids can be updated with penalized or compensated terms iteratively in order to 4nd near-global solution in optimal problem. The information transformed by DCT was separated into DC and AC coe5cients. Then, the AC coe5cients are trained by using the proposed methods to generate better codebook based on VQ. The compression performances using the proposed approaches are compared with FPCM and conventional VQ method. From the experimental results, the promising performances can be obtained using the proposed approaches. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

A competitive continuous Hopfield neural network for vector quantization in image compression

Vector quantization is a system in which a distortion function is minimized for multidimensional optimization problems. The purpose of such a system is data compression. In this paper, a parallel approach using the competitive continuous Hopfield neural network (CCHNN) is proposed for the vector quantization in image compression. In CCHNN, the codebook design is conceptually considered as a clustering problem. Here, it is a kind of continuous Hopfield network model imposed by the winner-take-all mechanism, working toward minimizing an objective function that is defined as the average distortion measure between any two training vectors within the same class (within-class). It also forward maximizes an objective function defined as the average distortion measure between any two training vectors in separate classes (between-class). For an image of n training vectors and c objects of interest, the proposed CCHNN would consist of n c neurons. Each neuron (or training vector) occupies l l components of a training vector. In the experimental results, the proposed method shows more promising results after convergence than the generalized Lloyd algorithm. # 1999 Elsevier Science Ltd. All rights reserved.

Medical image segmentation using mean field annealing network

This paper presents an unsupervised segmentation approach applying the mean field annealing (MFA) heuristic with the modified cost function. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to cluster centers. To resolve the optimal problem using a Hopfield or simulated annealing neural network, the penalty terms are combined into a weighted sum using several coefficients determined by user. Using the MFA network to medical image segmentation, the need for finding weighting factors in the energy function can be eliminated and the rate of convergence is much faster than that of simulated annealing. The experimental results show that good and valid solutions can be obtained using the MFA neural network.

Classification of multispectral images through a rough-fuzzy neural network

A new fuzzy Hopfield-model net based on rough-set reason- ing is proposed for the classification of multispectral images. The main purpose is to embed a rough-set learning scheme into the fuzzy Hopfield network to construct a classification system called a rough-fuzzy Hopfield net (RFHN). The classification system is a paradigm for the implementation of fuzzy logic and rough systems in neural network ar- chitecture. Instead of all the information in the image being fed into the neural network, the upper- and lower-bound gray levels, captured from a training vector in a multispectal image, are fed into a rough-fuzzy neuron in the RFHN. Therefore, only 2/N pixels are selected as the training samples if an N-dimensional multispectral image was used. In the simu- lation results, the proposed network not only reduces the consuming time but also reserves the classification performance.

Classification of multispectral images based on a fuzzy-possibilistic neural network

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic C-means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fuzzy logic systems in neural network architecture. Instead of one state in a neuron for the conventional Hopfield nets, each neuron occupies 2 states called membership state and typicality state in the proposed FPHN. The proposed network not only solves the noise sensitivity fault of Fuzzy C-means (FCM) but also overcomes the simultaneous clustering problem of possibilistic C-means (PCM) strategy. In addition to the same characteristics as the FPCM algorithm, the simple features of this network are clear potential in optimal problem. The experimental results show that the proposed FPHN can obtain better solutions in the classification of multispectral images.

Wireless Sensor Networks and Their Applications to the Healthcare and Precision Agriculture

Wireless connection based smart sensors network can combine sensing, computation, and communication into a single, small device. Because sensor carries its own wireless data transceiver, the time and the cost for construction, maintenance, the size and weight of whole system have been reduced. Information collected from these sensor nodes is routed to a sink node via different types of wireless communication approaches. Healthcare systems have restricted the activity area of patients to be within the medical health care center or residence area. To provide more a feasible situation for patients, it is necessary to embed wireless communication technology into healthcare systems. The physiological signals are then immediately transmitted to a remote management center for analysis using wireless local area network. Healthcare service has been further extended to become mobile care service due to the ubiquity of global systems for mobile communications and general packet radio service. It is important that using sensors to detect field-environment signals in agriculture is understood since a long time ago. Precision agriculture is a technique of management of large fields in order to consider the spatial and temporal variability. To use more sophisticated sensor devices with capabilities of chemical and biological sensing not only aids the personnel in the field maintenance procedure but also significantly increases the quality of the agricultural product. In this chapter, we examine the fields in healthcare and precision agriculture based on wireless sensor networks. In the application of healthcare systems, a System on a Chip (SoC) platform and Bluetooth wireless network technologies were combined to construct a wireless network physiological signal monitoring system. In the application of precision agriculture, an SoC platform was also used combining the ZigBee technology to consist a field signals monitoring system. In addition to the two applications, the fault tolerance in wireless sensor networks is also discussed in this chapter.

A PC-based imaging system for automated platelet identification

A PC-based imaging system has been developed for automatically identifying fluorescence-labeled individual platelets adherent to protein-coated surface under flow conditions. It is used to eliminate the laborious and time-consuming task and the subjective error of manual measurements. A three-pass image processing was developed for platelet identification. From the results, 90-95% accuracy could be routinely obtained. The platelet distribution and other related parameters could be easily extracted and investigated.<<ETX>>