Structuring element

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

Multiresolution decomposition and adaptive filtering with rank order based filters-application to defect detection

Abstract: A class of nonlinear filters called rank order based filters, including, as special cases, rank order and morphological (with flat structuring element) filters, are dealt with. How they achieve size-sensitive multiresolution decompositions is described. The necessity of a special processing stage dedicated to shape preservation is shown, and a solution is proposed. The problem of filter mask (or structuring element) optimization to minimize a statistical criterion is also studied. The proposed algorithms are very simple, and their derivation is performed by using an implicit formulation of the filtering process. Both techniques are applied to a practical defect detection problem. >

Image processing method and image inspecting method

Abstract: The present invention relates to an image processing method and image inspecting method with high versatility which enable efficient and highly accurate proof of authenticity of a digital image. The image processing method subjects at least a part of a digital image which can exist temporarily or continuously in a falsification-vulnerable environment, to a first morphology operation using a predetermined structuring element, to process the digital image. The image inspecting method subjects the digital image thus processed, to a second morphology operation using the same structuring element as in the first morphology operation. The morphology operations have the idempotent and the presence or absence of falsification can be detected by determining identity of images before and after the second morphology operation.

Comparison of extracted skeleton patterns of human femur and humerus using the line skeleton operation process and skeleton operation process

Abstract: This study presents and compares trabecular skeletal patterns of human femoral and humeral bone extracted by applying mathematical morphology and morphological filters. Two morphological filters and skeleton processing operations were used to extract the skeleton images. Images obtained with a combination of the single disc-shaped structuring element (5 pixel diameter) and the skeleton operation were compared to images obtained with the line structuring element and the line skeleton operation. Seven dentomaxillofacial radiologists evaluated the resulting images. Line skeleton images (both subset and sumset images) had fewer trabecular elements extracted and higher discontinuity as the operation number increased, and there were obvious differences between the simple skeleton images and the line skeleton images. With more study these differences may be useful in detecting changes in trabecular patterns resulting from disease, stress or altered function.

Texture Classification Using Neural Networks and Local Granulometries

Abstract: This paper presents a method for segmenting interstitium and tubules in images of kidneys’ biopsies. Openings by structuring elements of increasing size, forming a granulometry, were performed on the entire image. For every pixel x and for each size of the structuring element the volume over a small window centered at x was measured (a local Granulometry). The vectors defined as the volume gradient served as an entry to a neural network (NN). The NN was taught to discriminate between vectors corresponding to pixels of the interstitium (textured region) and vectors corresponding to pixels of the tubules (non-textured region). The correlation factor between the area of the interstitium and the renal function was computed and compared to the results obtained with the manual procedure and two other automatic procedures.

Shape recognition using morphological transformations

Abstract: A morphological hit-or-miss algorithm is presented which can distinguish a distorted shape from among others and locate it. Shape recognition is presented in noisy environments as a problem of recognizing imperfect shapes. The method employs only one hit-or-miss operation. The resultant shape location is presented by a small cluster of points within a known set. The role of window W in the hit-or-miss transformation is discussed, and it is shown that the boundary of the complement of the shape, delta A/sup c/, can be used as a structuring element on the complement of the image, I/sup c/, with the result of the hit-or-miss transformation being the same as that when a window is used in the transformation. Without a window restriction, the hit-or-miss transformation is more practical in shape recognition. >