Wyniki wyszukiwania - Biblioteka Nauki

1

Surrogate data: A novel approach to object detection

100%

Tabor Z.

|

tom 20

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nr 3

545-553

EN

In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.

2

Surrogate data: A novel approach to object detection

75%

Tabor Z.

International Journal of Applied Mathematics and Computer Science

|

2010

|

tom Vol. 20, no 3

545-553

EN

In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.

3

3D visualization of segmented cruciate ligaments

63%

Badura P.

|

2006

|

tom Vol. 10

73--79

EN

A fuzzy approach to segmentation of the cruciate ligaments of the knee joint and a three dimensional visualisation method are presented in this paper. The cruciate ligaments are the major stabilizers of the knee. The ligaments injuries are common nowadays and a correct diagnostics, preceding the surgical therapy is a very important task. Segmentation of the ligaments is difficult due to a poor visibility of edges in some cases of injuries and their appearance on a small number of slides at Magnetic Resonance Imaging (MRI). The method described here is based on fuzzy connectedness principles. It creates a fuzzy connectivity scene by assigning a strength of connectedness to each possible path between some predefined seed point and any other image element. Then such scene is thresholded to produce final segmentation result. The conventional fuzzy connectedness method with Dijkstra algorithm for creating the fuzzy connectivity scene has been implemented in a 3D space. The object, being the result of segmentation process, is visualised in the Visualisation Toolkit (VTK) environment. The method has been tested on a set of images. An example of its performance is shown along with some plans for future research.