Measurements of digitized objects with fuzzy borders in 2D and 3D

Sladoje, Nataša; Nyström, Ingela; Saha, Punam K.

doi:10.1016/j.imavis.2004.06.011

Cited by 43 publications

(30 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous work, it is shown that the precision of estimates of various properties of a continuous original shape is highly improved when derived from a fuzzy, instead of a crisp representation of a continuous object [4]. In this paper, we analyse the accuracy of the estimation of moments, when they are calculated from a fuzzy representation of a shape.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Moments of Digitized Objects with Fuzzy Borders

Sladoje

Lindblad

2005

Image Analysis and Processing – ICIAP 2005

Self Cite

View full text Add to dashboard Cite

Abstract. Error bounds for estimation of moments from a fuzzy representation of a shape are derived, and compared with estimations from a crisp representation. It is shown that a fuzzy membership function based on the pixel area coverage provides higher accuracy of the estimates, compared to binary Gauss digitization at the same spatial image resolution. Theoretical results are confirmed by a statistical study of disks and squares, where the moments of the shape, up to order two, are estimated from its fuzzy discrete representation. The errors of the estimates decrease both with increased size of a shape (spatial resolution) and increased membership resolution (number of available grey-levels).

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of Moments of Digitized Objects with Fuzzy Borders

Sladoje

Lindblad

2005

Image Analysis and Processing – ICIAP 2005

Self Cite

View full text Add to dashboard Cite

show abstract

“…Volume is easily estimated by counting the number of voxels that constitute an object. A number of surface area estimates of 3D objects exist in the literature [8,11,30]. A straightforward and simple way to obtain a surface area estimate of a 3D object is to count the number of foreground voxels with a surface neighbour in the background as in [19].…”

Section: Size Based Attributesmentioning

confidence: 99%

“…A straightforward and simple way to obtain a surface area estimate of a 3D object is to count the number of foreground voxels with a surface neighbour in the background as in [19]. A more accurate surface area estimate is obtained through approximating the boundary of a triangular representation, using the marching cubes algorithm [13,30]. X-extent, Y-extent and Z-extent are computed from the minimum and maximum x, y, and z coordinates values of pixels within each peak component.…”

Section: Size Based Attributesmentioning

confidence: 99%

Cluster Based Vector Attribute Filtering

Kiwanuka

Wilkinson

2016

Mathematical Morphology - Theory and Applications

View full text Add to dashboard Cite

Morphological attribute lters operate on images based on properties or attributes of connected components. Until recently, attribute ltering was based on a single global threshold on a scalar property to remove or retain objects. A single threshold struggles in case no single property or attribute value has a suitable, usually multi-modal, distribution. Vector-attribute ltering allows better description of characteristic features for 2D images. In this paper, we apply vector-attribute ltering to 3D and incorporate unsupervised pattern recognition, where connected components are classi ed based on the similarity of feature vectors. Using a single attribute allows multi-thresholding for attribute lters where more than two classes of structures of interest can be selected. In vector-attribute lters automatic clustering avoids the need for either setting very many attribute thresholds, or nding suitable class prototypes in 3D and setting a dissimilarity threshold. Explorative visualization reduces to visualizing and selecting relevant clusters. We show that the performance of these new lters is better than those of regular attribute lters in enhancement of objects in medical images.

show abstract

“…By allowing mixed labels, it is possible to obtain segmentations with sub-pixel precision. Numerous studies have confirmed that pixel coverage segmentation [14] outperforms crisp segmentation for subsequent measuring of object properties such as length and area/volume, see, e.g., [13,15]. In [9], it is shown that consequently misplacing the tissue borders, in a brain volume having voxels of size 1 mm 3 , by one voxel resulted in volume errors of approximately 30%, 40% and 60% for white matter, grey matter and cerebrospinal fluid, respectively.…”

Section: Introductionmentioning

confidence: 96%

Sub-pixel Segmentation with the Image Foresting Transform

Malmberg

Lindblad

Nyström

2009

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. The Image Foresting Transform (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned user-defined labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. In its original form, the IFT produces crisp segmentations, i.e., each image element is assigned the label of exactly one seed-point. Here, we propose a modified version of the IFT that computes region boundaries with sub-pixel precision by allowing mixed labels at region boundaries. We demonstrate that the proposed sub-pixel IFT allows properties of the segmented object to be measured with higher precision.

show abstract

Measurements of digitized objects with fuzzy borders in 2D and 3D

Cited by 43 publications

References 25 publications

Estimation of Moments of Digitized Objects with Fuzzy Borders

Estimation of Moments of Digitized Objects with Fuzzy Borders

Cluster Based Vector Attribute Filtering

Sub-pixel Segmentation with the Image Foresting Transform

Contact Info

Product

Resources

About