Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Spike separation from EEG/MEG data using morphological filter and wavelet transform.

Abstract: In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations.

Morphological image processing for locating minelike objects from side-scan sonar images

Abstract: A morphological image-processing algorithm was developed to facilitate the rapid identification of bottom minelike objects in side scan sonar acoustic backscatter images. Because large numbers of images are being processed, the emphasis is on rapid computation. The algorithm achieves computational efficiency by dividing the image into bins of a pre-chosen size and performing a binary opening operation for each bin with a 2 X 2 structuring element on each bin having sufficient pixels within the threshold range. Thresholding can be either above a high backscatter level to highlight bright proud objects or below a low backscatter level to highlight low backscatter shade-like objects. The morphological operating highlights continuous pixels within the threshold range without distortion and eliminates objects smaller than the structuring element. A connected component algorithm was used to locate all identified contiguous pixels and to tabulate their centroids and sizes. The identified objects were then screened as possible targets by checking the proximity of bright and dark objects within some threshold radius and chosen direction of each other. The chosen targets were either graphed or archived. Algorithm performance was evaluated by comparison with other target identification algorithms and was found to be compatible. An advanced interactive mode allows using different structuring elements and different morphological operations for possible improvement of the batch mode algorithm. The algorithm, while potentially effective for target identification, is primarily useful for false target identification. By operating on standard survey images the algorithm can isolate areas of potential false target proliferation. Spatial statistical methods, based on k- nearest neighbor distributions and hierarchical and k-means clustering were used to delineate regions of high false target density within the survey area.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Simd algorithm for image dilation and erosion processing

Abstract: Systems and methods may receive an input image for data processing, divide the input image into a plurality of blocks, each block including a plurality of rows, and each row including a plurality of pixels and process each pixel in the input image within a row in parallel with a user-defined template. In one example, the user-defined template is to include a structuring element and a row pixel mask.

Tumor segmentation of multi-echo MR T2-weighted images with morphological operators

Abstract: In the present work an automatic brain tumor segmentation procedure based on mathematical morphology is proposed. The approach considers sequences of eight multi-echo MR T2-weighted images. The relaxation time T2 characterizes the relaxation of water protons in the brain tissue: white matter, gray matter, cerebrospinal fluid (CSF) or pathological tissue. Image data is initially regularized by the application of a log-convex filter in order to adjust its geometrical properties to those of noiseless data, which exhibits monotonously decreasing convex behavior. Finally the regularized data is analyzed by means of an 8-dimensional morphological eccentricity filter. In a first stage, the filter was used for the spatial homogenization of the tissues in the image, replacing each pixel by the most representative pixel within its structuring element, i.e. the one which exhibits the minimum total distance to all members in the structuring element. On the filtered images, the relaxation time T2 is estimated by means of least square regression algorithm and the histogram of T2 is determined. The T2 histogram was partitioned using the watershed morphological operator; relaxation time classes were established and used for tissue classification and segmentation of the image. The method was validated on 15 sets of MRI data with excellent results.

Efficient algorithm for computing the antigranulometry

Abstract: The principle of shape recognition by using granulometries, is to transform a binary planar shape into a curve which is translation, rotation and scale invariant by a family of morphological transformations depending on the size of the structuring element. This paper presents a fast algorithm for the computation of the anti-granulometry by closings which avoids boundary effects. This algorithm is based on a simple geometric transformation called the `Steiner Inverse Transform', on which original theoretical results are given.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.