Topic
Structuring element
About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.
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TL;DR: A morphological approach to cell image segmentation, that is, more accurate than the classical watershed-based algorithm, is introduced for detecting and classifying malaria parasites in images of Giemsa stained blood slides.
291 citations
TL;DR: Two novel methods which can develop optimizing Top-Hat morphological filtering parameters are presented for spot target detection, one based on neural network and the other based on genetic algorithm.
258 citations
01 Jan 1994
TL;DR: Grey-scale area openings and closings can be seen as transformations with a structuring element which locally adapts its shape to the image structures, and therefore have very nice filtering capabilities.
Abstract: The filter that removes from a binary image the components with area smaller than a parameter λ is called area opening. Together with its dual, the area closing, it is first extended to grey-scale images. It is then proved to be equivalent to a maximum of morphological openings with all the connected structuring elements of area greater than or equal to λ. The study of the relationships between these filters and image extrema leads to a very efficient area opening/closing algorithm. Grey-scale area openings and closings can be seen as transformations with a structuring element which locally adapts its shape to the image structures, and therefore have very nice filtering capabilities. Their effect is compared to that of more standard morphological filters. Some applications in image segmentation and hierarchical decomposition are also briefly described.
253 citations
TL;DR: The algorithms described in this paper are capable of performing the basic morphological image transforms using structuring elements of arbitrary size and shape, using a bitmap representation of binary images instead of representing binary images as bitplanes inserted in gray value images.
250 citations
TL;DR: An approach to QRS complex detection based on mathematical morphology is presented, which works as a peak-valley extractor and it is controlled by the shape of the structuring element, resulting in very fast execution times.
Abstract: An approach to QRS complex detection based on mathematical morphology is presented. QRS complexes are detected by the application of a simple morphological operator. This operator works as a peak-valley extractor and it is controlled by the shape of the structuring element. A set (horizontal line segment) is used as a structuring element, resulting in very fast execution times. The accuracy of this approach has been tested using a standard ECG library; a sensitivity of 99.38% and a positive predictivity of 99.48% have been achieved. >
213 citations