Multi-scale Mathematical Morphology Research Articles

Wear Particles Image Segmentation Method Based on Multiscale Mathematical Morphology

Image segmentation plays an important role in wear particles analysis. A new segmentation method based on multiscale mathematical morphology is proposed for wear particles image segmentation. The newly introduced method employs different scale structuring elements to detect the image edge, the final edge is calculated by the weighted average method. Edge details can be remained by small scale structuring element (SE) and noise can be depressed effectively by large scale SE, therefore, the new method has great effect in edge accuracy, strong and weak edge extraction and noise suppression. The efficiency of the method is evaluated by a set of wear particles images. The comparison with the single scale SE and other traditional methods demonstrates the improvement of the new algorithm.

QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks

A novel wearable electrocardiograph (ECG) QRS detection algorithm for wearable ECG devices in body area networks is presented in this paper, which utilizes the multistage multiscale mathematical morphology filtering to suppress the impulsive noise and uses the multiframe differential modulus accumulation to remove the baseline drift and enhance the signal. The proposed algorithm, verified with data from the MIT/BIH Arrhythmia Database and wearable ECG devices, achieves an average QRS detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%. It compares favorably to the published methods.

Automatic diatom identification using contour analysis by morphological curvature scale spaces

A method for automatic identification of diatoms (single-celled algae with silica shells) based on extraction of features on the contour of the cells by multi-scale mathematical morphology is presented. After extracting the contour of the cell, it is smoothed adaptively, encoded using Freeman chain code, and converted into a curvature representation which is invariant under translation and scale change. A curvature scale space is built from these data, and the most important features are extracted from it by unsupervised cluster analysis. The resulting pattern vectors, which are also rotation-invariant, provide the input for automatic identification of diatoms by decision trees and k-nearest neighbor classifiers. The method is tested on two large sets of diatom images. The techniques used are applicable to other shapes besides diatoms.