The Experts below are selected from a list of 10203 Experts worldwide ranked by ideXlab platform

Hiroshi Shibasaki - One of the best experts on this subject based on the ideXlab platform.

  • automatic detection of photic evoked spikes contaminated with slow burst by use of Morphological Filter and similarity coefficient
    International Conference on Complex Medical Engineering, 2012
    Co-Authors: Shigeto Nishida, Hiroshi Shibasaki, Takenao Sugi, Akio Ikeda, Takashi Nagamine, Masatoshi Nakamura
    Abstract:

    A method for detecting spikes and slow burst in photic evoked electroencephalogram (EEG) was proposed. The spikes were detected by combining methods of the Morphological Filter and the similarity coefficient in the time domain. The slow burst was detected by using pole of AR model (auto-regressive model) in the frequency domain. The proposed method was applied for the photic evoked EEG data containing spikes and slow burst, and brought satisfactory coincidence with the results interpreted by a qualified electroencephalographer.

  • Brief A Morphological Filter for extracting waveform characteristics of single-sweep evoked potentials
    Automatica, 1999
    Co-Authors: Shigeto Nishida, Masatoshi Nakamura, Kazuo Shindo, Masutaro Kanda, Hiroshi Shibasaki
    Abstract:

    A Morphological Filter was designed for extracting the waveform characteristics of pain SEP from the single-sweep record. The properties of the basic operations for the Morphological Filter; erosion, dilation, opening and closing were clarified in order to design an appropriate Filter. The Morphological Filter was designed by selecting the structuring elements, which represented the features of the pain SEP waveform. The designed Morphological Filter was evaluated using the simulation data, and applied to the pain SEP data obtained from a normal adult with satisfactory results.

Isamu Kashima - One of the best experts on this subject based on the ideXlab platform.

  • Basic study of three-dimensional fine vascular structural analysis based on Morphological processing
    Oral Radiology, 2013
    Co-Authors: Ryota Kawamata, Takashi Sakurai, Isamu Kashima
    Abstract:

    Objective To develop a new evaluation method that utilizes three-dimensional Morphological Filter processing for the analysis of fine vascular structures. Methods Three-dimensional Morphological Filter processing was applied to a simulated vascular image to extract a binary skeletal pattern. To assess the precision of the proposed method, grayscale test charts were created using standard graphics software and evaluated visually. In addition, to access the accuracy of the grayscale test charts, phantom images with varied densities and structures were obtained by computed tomography and a quantitative evaluation was performed by calculation of Morphological indices and a node-strut analysis. Results Application of the three-dimensional Morphological Filter allowed fine vascular structures to be extracted as binary skeletal patterns. In the quantitative analysis, all parameters showed strong correlations ( R ^2 = 0.983–1.000) between the theoretical and measured values. Conclusion The results suggest that the new method has potential for three-dimensional analysis of fine vascular structures.

  • structuring of parameters for assessing vertebral bone strength by star volume analysis using a Morphological Filter
    Journal of Bone and Mineral Metabolism, 2001
    Co-Authors: Tetsuo Tanaka, Takashi Sakurai, Isamu Kashima
    Abstract:

    Based on the relationship between bone strength, trabecular skeletal structure, and bone mineral density (BMD), structure parameters for assessing vertebral bone strength were studied using 18 cancellous bone blocks from the third lumbar vertebra of elderly persons. The trabecular bone pattern of each bone block was binarized into a trabecular skeletal pattern by computed radiography (CR) using a Morphological Filter. The binarized trabecular skeletal pattern was quantified into a trabecular skeletal pixel percentage (SKP; volume parameter of the trabecular skeletal signal component) and trabecular skeletal star volume (Vt; connection parameter of the trabecular skeletal structure) by star volume analysis. Then, the BMD and elasticity of each bone block were measured by dual X-ray absorptiometry and mechanical tests to determine the correlation between SKP and Vt. In the present study, no significant correlations were observed between BMD and elasticity. Elasticity differed greatly between some bone blocks even though BMD was essentially the same. Grid-like skeletal patterns consisting of vertical and horizontal continuous lines showed higher elasticity. SKP showed higher correlations with elasticity than BMD in subset (n = 2–7) and sumset images, although the fluctuation range was narrow. Meanwhile, Vt showed higher correlations with elasticity than SKP in subset (n = 1–6) and sumset images. Vt showed stronger correlations with elasticity than SKP. This fact indicates that strong relationships exist between the connectivity of trabecular skeletal structure and bone strength. Because the SKP–BMD and Vt–BMD correlations are weak, the influences exerted by SKP and Vt seem to be independent of the influence of BMD on the bone strength of vertebra obtained from persons with advanced age. These results indicate that SKP and Vt obtained by star volume analysis using a Morphological Filter are effective as structure parameters for analyzing the bone quality of lumbar vertebra.

  • quantitative analysis using the star volume method applied to skeleton patterns extracted with a Morphological Filter
    Journal of Bone and Mineral Metabolism, 2000
    Co-Authors: Akitoshi Ikuta, Satsuki Kumasaka, Isamu Kashima
    Abstract:

    In this study, a Morphological Filter was combined with star volume analysis and applied to digital images to determine its potential usefulness in assessing trabecular structure. Three digital "geometric" test patterns (square, rectangle, circle) were created on a CRT (cathode ray tube). Each shape was arranged into five groups by size to yield 15 final "skeletal" patterns that were subsequently assessed with star volume analysis. Also, three digital X-ray images (background, soft tissue, bone block) were processed with a Morphological Filter to create three sets of 11 skeletal patterns each. These patterns were also assessed with star volume analysis and the ratio of extracted skeletal elements (in pixel numbers) to total pixel numbers was expressed as the pixel percentage. Star volume analysis was then applied to these digital skeletal images to yield the volume of extracted "skeletal" trabecular elements (Vsk) and the volume of nonskeletal (marrow space) elements (Vsp). The Vsk and Vsp were compared for all the different skeletal patterns. The pixel percentages were then compared to the star volume results for the X-ray test patterns. The Vsk decreased and Vsp increased as the number of operations (n) increased for both digital X-ray images and the geometric test patterns when the X-ray images were depicted by pixel percentages. Also, all true bone test patterns were clearly different both visually and quantitatively when compared to the noise skeletons extracted from background and soft tissue. Therefore, as Vsk was increased, so was connectivity. It can be concluded that the application of Morphological Filters and star volume analysis may be a useful tool in quantitatively determining the characteristics and continuity of trabecular skeletal structures. Further studies involving a larger number of bone samples and using models to compare measurements of calculated versus actual volume should reveal the true potential of this method for evaluating bone structure and its relationship to bone strength and also increase the tools available for evaluating bone diseases such as osteoporosis.

Shigeto Nishida - One of the best experts on this subject based on the ideXlab platform.

  • automatic detection of photic evoked spikes contaminated with slow burst by use of Morphological Filter and similarity coefficient
    International Conference on Complex Medical Engineering, 2012
    Co-Authors: Shigeto Nishida, Hiroshi Shibasaki, Takenao Sugi, Akio Ikeda, Takashi Nagamine, Masatoshi Nakamura
    Abstract:

    A method for detecting spikes and slow burst in photic evoked electroencephalogram (EEG) was proposed. The spikes were detected by combining methods of the Morphological Filter and the similarity coefficient in the time domain. The slow burst was detected by using pole of AR model (auto-regressive model) in the frequency domain. The proposed method was applied for the photic evoked EEG data containing spikes and slow burst, and brought satisfactory coincidence with the results interpreted by a qualified electroencephalographer.

  • Brief A Morphological Filter for extracting waveform characteristics of single-sweep evoked potentials
    Automatica, 1999
    Co-Authors: Shigeto Nishida, Masatoshi Nakamura, Kazuo Shindo, Masutaro Kanda, Hiroshi Shibasaki
    Abstract:

    A Morphological Filter was designed for extracting the waveform characteristics of pain SEP from the single-sweep record. The properties of the basic operations for the Morphological Filter; erosion, dilation, opening and closing were clarified in order to design an appropriate Filter. The Morphological Filter was designed by selecting the structuring elements, which represented the features of the pain SEP waveform. The designed Morphological Filter was evaluated using the simulation data, and applied to the pain SEP data obtained from a normal adult with satisfactory results.

  • a Morphological Filter for extracting individual waveform characteristics of single sweep evoked potentials
    IFAC Proceedings Volumes, 1997
    Co-Authors: Shigeto Nishida, Masatoshi Nakamura, Kazuo Shindof, Hiroshi Shibasakij
    Abstract:

    Abstract In order to extract the waveform characteristics of the pain SEP from the single sweep record, a Morphological Filter was designed. The properties of the basic operations for the Morphological Filter; erosion, dilation, opening and closing were clarified. Based on these properties, the Morphological Filter was designed by selecting the structuring elements, which appropriately represented the features of the pain SEP waveform. The designed Morphological Filter was evaluated using the simulation data, and applied to the pain SEP data obtained from a normal adult.

Masatoshi Nakamura - One of the best experts on this subject based on the ideXlab platform.

  • automatic detection of photic evoked spikes contaminated with slow burst by use of Morphological Filter and similarity coefficient
    International Conference on Complex Medical Engineering, 2012
    Co-Authors: Shigeto Nishida, Hiroshi Shibasaki, Takenao Sugi, Akio Ikeda, Takashi Nagamine, Masatoshi Nakamura
    Abstract:

    A method for detecting spikes and slow burst in photic evoked electroencephalogram (EEG) was proposed. The spikes were detected by combining methods of the Morphological Filter and the similarity coefficient in the time domain. The slow burst was detected by using pole of AR model (auto-regressive model) in the frequency domain. The proposed method was applied for the photic evoked EEG data containing spikes and slow burst, and brought satisfactory coincidence with the results interpreted by a qualified electroencephalographer.

  • Brief A Morphological Filter for extracting waveform characteristics of single-sweep evoked potentials
    Automatica, 1999
    Co-Authors: Shigeto Nishida, Masatoshi Nakamura, Kazuo Shindo, Masutaro Kanda, Hiroshi Shibasaki
    Abstract:

    A Morphological Filter was designed for extracting the waveform characteristics of pain SEP from the single-sweep record. The properties of the basic operations for the Morphological Filter; erosion, dilation, opening and closing were clarified in order to design an appropriate Filter. The Morphological Filter was designed by selecting the structuring elements, which represented the features of the pain SEP waveform. The designed Morphological Filter was evaluated using the simulation data, and applied to the pain SEP data obtained from a normal adult with satisfactory results.

  • a Morphological Filter for extracting individual waveform characteristics of single sweep evoked potentials
    IFAC Proceedings Volumes, 1997
    Co-Authors: Shigeto Nishida, Masatoshi Nakamura, Kazuo Shindof, Hiroshi Shibasakij
    Abstract:

    Abstract In order to extract the waveform characteristics of the pain SEP from the single sweep record, a Morphological Filter was designed. The properties of the basic operations for the Morphological Filter; erosion, dilation, opening and closing were clarified. Based on these properties, the Morphological Filter was designed by selecting the structuring elements, which appropriately represented the features of the pain SEP waveform. The designed Morphological Filter was evaluated using the simulation data, and applied to the pain SEP data obtained from a normal adult.

Xihui Liang - One of the best experts on this subject based on the ideXlab platform.

  • train axle bearing fault detection using a feature selection scheme based multi scale Morphological Filter
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Xihui Liang, Jianhui Lin, Yuejian Chen, Jianxin Liu
    Abstract:

    Abstract This paper presents a novel signal processing scheme, feature selection based multi-scale Morphological Filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a Filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

  • diagonal slice spectrum assisted optimal scale Morphological Filter for rolling element bearing fault diagnosis
    Mechanical Systems and Signal Processing, 2017
    Co-Authors: Yifan Li, Xihui Liang
    Abstract:

    Abstract This paper presents a novel signal processing scheme, diagonal slice spectrum assisted optimal scale Morphological Filter (DSS-OSMF), for rolling element fault diagnosis. In this scheme, the concept of quadratic frequency coupling (QFC) is firstly defined and the ability of diagonal slice spectrum (DSS) in detection QFC is derived. The DSS-OSMF possesses the merits of depressing noise and detecting QFC. It can remove fault independent frequency components and give a clear representation of fault symptoms. A simulated vibration signal and experimental vibration signals collected from a bearing test rig are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearing. In addition, comparisons are performed between a multi-scale Morphological Filter (MMF) and a DSS-OSMF. DSS-OSMF outperforms MMF in detection of an outer race fault and a rolling element fault of a rolling element bearing.