International Seminar on Future BioMedical Information Engineering 

About: International Seminar on Future BioMedical Information Engineering is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Algorithm design. Over the lifetime, 117 publications have been published by the conference receiving 486 citations.

A Modified FCM Algorithm for MRI Brain Image Segmentation

Abstract: Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain image Fuzzy c-means (FCM) clustering algorithm has been widely used in many medical image segmentations However, the conventionally standard FCM algorithm is sensitive to noise because of not taking into account the spatial information To overcome the above problem, a modified FCM algorithm (called mFCM later) for MRI brain image segmentation is presented in this paper The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster The proposed algorithm is applied to both artificial synthesized image and real image Segmentation results not only on synthesized image but also MRI brain image which degraded by Gaussian noise and salt-pepper noise demonstrates that the presented algorithm performs more robust to noise than the standard FCM algorithm

Realization and Application Research of BP Neural Network Based on MATLAB

Abstract: This paper simply depicts knowledge related to BP network and the algorithm first, then introduces BP tool functions supplied by Matlab for BP neural network research and how to program within the functions; finally explains the advantages supplied by BP tool functions for BP neural network research with BP neural application in pattern identification and curve imitation.

An Improved Design for the IIR-Type Digital Fractional Order Differential Filter

Abstract: In this paper, a novel design for improving the fractional order differential filter is put forward. By analyzing the frequency characteristic of typical fractional order differential filter, it can be seen that these kinds of differential filters have merits and demerits respectively and also could be complementary each other. So based on these features, three kinds of novel first order differential filters are constructed by the interpolated method. And then we choose a differential filter from these three kinds of filters which has much better frequency characteristic, also the improved IIR-type fractional order differential filter will be obtained by the method of continuous fraction expansion (CFE). The experiment result shows that the frequency response of the improved fractional order differential filter is more approximate to the ideal fractional order differential filter. And it also shows that the method put forward in the paper can improve the performance of the fractional order differential filter obviously under the premise of not increasing the structure complexity of the filter.

Extracting Regions of Interest in Biomedical Images

Abstract: Regions of interest (ROI) usually means the meaningful and important regions in the images The use of ROI can avoid the processing of irrevelent image points and accelerate the processing Extraction of regions of interest from images is an important and unsolved topic in the image processing area, especially in biomedical image processing area In this paper, a feasible and fast constrast-based method is proposed to extract regions of interest in biomedical images Motivated biologically, this approach simulates the bottom-up human visual selective attention mechanism, computes the global contrast of each pixel and constructs the saliency mapThen by segmentating the saliency map using a dynamic threshold, the regions of interest can be extracted This approach has been tested on many medical images Computer experimental results show that the proposed method can extract regions of interste in biomedical images rapidly and precisely, and indicate that the proposed approach is effective and practicable

An Improved Apriori Algorithm Based on the Matrix

Abstract: A priori algorithm is a classical algorithm of association rule mining and also is one of the most important algorithms But it also has some limitations It produces overfull candidates of frequent itemsets, so the algorithm needs scan database frequently when finding frequent itemsets So it must be inefficient To solve the bottleneck of the a priori algorithm, this paper introduces an improved algorithm based on the matrix It uses the matrix effectively indicate the affairs in the database and uses the "AND operation" to deal with the matrix to produce the largest frequent itemsets and others It needn't scan the database time and again to lookup the affairs, and also greatly reduce the number of candidates of frequent itemsets This paper uses an example to analyze and compare the difference between the two algorithms and the result shows that the improved algorithm obtains the bonus time of calculating and promotes the efficiency of computing