Time-Frequency Spectrogram Segmentation Using the Multi-Scale Morphological Gradient and the Marked Watershed Algorithm

doi:10.3969/j.cnki.jdxb.2015.04.004

journal6 ›› 2015, Vol. 36 ›› Issue (4): 12-17.DOI: 10.3969/j.cnki.jdxb.2015.04.004

• Information and computer • Previous Articles Next Articles

Time-Frequency Spectrogram Segmentation Using the Multi-Scale Morphological Gradient and the Marked Watershed Algorithm

GUO Hai-Tao, XU Lei, ZHAO Hong-Ye, TIAN Yuan-Yuan, JIAO Sheng-Xi

(1.College of Electronic Information Engineering，Inner Mongolia University，Hohhot 010021，China;2.Electrical Engineering College，Northeast Dianli University，Jilin 132012，Jilin China;3.School of Automation Engineering，Northeast Dianli University，Jilin 132012，Jilin China;4.School of Mechanical Engineering，Northeast Dianli University，Jilin 132012，Jilin China)

Online:2015-07-25 Published:2015-07-27

Abstract

Abstract: It is important and difficult to segment a time-frequency spectrogram due to its strong interference as well as serious overlap among targets and between targets and interference.An image entropy based fusion method for multi-scale morphological gradient image of a time-frequency spectrogram is presented.By combining that method with the marked watershed algorithm，a method for segmenting time-frequency spectrogram based on the multi-scale morphological gradient image and the marked watershed algorithm，is obtained.The experiment results show that it is more practical than the segmentation method which is based on single scale morphological gradient and marker watershed，and more accurate than Otsu method.

Key words: time-frequency spectrogram, multi-scale morphological gradient image, image entropy, watershed algorithm, image segmentation

GUO Hai-Tao, XU Lei, ZHAO Hong-Ye, TIAN Yuan-Yuan, JIAO Sheng-Xi. Time-Frequency Spectrogram Segmentation Using the Multi-Scale Morphological Gradient and the Marked Watershed Algorithm[J]. journal6, 2015, 36(4): 12-17.

References

[1] 蔡艳平，李艾华，王涛，等.基于时频谱图与图像分割的柴油机故障诊断[J].内燃机学报，2011，29（2）:181-186.

[2] GAVROVSKA A M，PASKAS M P，DUJKOVIC D，et al.Region-Based Phonocardiogram Event Segmentation in Spectrogram Image[C].IEEE:The 10th Symposium on Neural Network Applications in Electrical Engineering，2010:69-72.

[3] GAVROVSKA A M，PASKA M P，RELJIN I S.Determination of Morphologically Characteristic PCG Segments from Spectrogram Image[J].Telfor Journal，2010，2(2):4-77.

[4] SHAFI I，AHMAD J，SHAH S I，et al.Time Frequency Image Analysis Using Neural Networks[C].IEEE:IMACS Multiconference on Computational Engineering in Systems Applications，2006:315-320.

[5] LEPRETTRE B，MARTIN N.Extraction of Pertinent Subsets from Time-Frequency Representations for Detection and Recognition Purposes[J].Signal Processing，2002，82(2):229-238.

[6] STEINBERG R，O’SHAUGHNESSY D.Segmentation of a Speech Spectrogram Using Mathematical Morphology[C].IEEE:IEEE International Conference on Acoustics，Speech and Signal Processing，2008:1 637 -1 640.

[7] YUWONO M，SU S W，MOULTON B D，et al.Gait Cycle Spectrogram Analysis Using a Torso-Attached Inertial Sensor[C].IEEE:Annual International Conference of the IEEE Engineering in Medicine and Biology Society，2012:6 539-6 542.

[8] OZA A，CANAGARAJAH N，BULL D.Region Feature-Based Segmentation of Time-Frequency Images[C].Poznan，Poland:The 11th International Workshop on Systems，Signals and Image Processing，2004:375-378.

[9] ZHANG Zhenggang，XU Kailiang，TA Dean，et al.Joint Spectrogram Segmentation and Ridge-Extraction Method for Separating Multimodal Guided Waves in Long Bones[J].Science China-Physics，Mechanics and Astronomy，2013，56(7):1 317-1 323.

[10] MUKHOPADHYAY S，CHANDA B.An Edge Preserving Noise Smoothing Technique Using Multiscale Morphology[J].Signal Processing，2002，82:527-544.

[11] WANG D.A Multiscale Gradient Algorithm for Image Segmentation Using Watersheds[J].Pattern Recognition，1997，30(12):2 043-2 052.

[12] SALEMBIER P.Morphological Multiscale Segmentation for Image Coding[J].Signal Processing，1994，38(3):359-386.

[13] VINCENT L，SOILLE P.Watersheds in Digital Spaces:An Efficient Algorithm Based on Immersion Simulations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，1991，13(6):583-598.

[14] PUN T，KAPUR J N，WESZKA J S.Entropic Thresholding:A New Approach[J].Computer Vision，Graphics and Image Processing，1981，16(3):210-239.

[15] WU Qiang，MERCHANT F A，CASTLEMAN K R.Microscope Image Processing[M].Waltham，Massachusetts:Academic Press Elsevier，2008:135-142.

[16] SOILLE P.Morphological Image Analysis:Principle and Applications[M].New York:Springer-Verlag，2003.

[17] LEPRETTRE B，MARTIN N.Extraction of Pertinent Subsets from Time-Frequency Representations for Detection and Recognition Purposes[J].Signal Processing，2002，82(2):229-238.

Time-Frequency Spectrogram Segmentation Using the Multi-Scale Morphological Gradient and the Marked Watershed Algorithm

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