We present a model-driven approach to the segmentation of nasal cavity and paranasal sinus boundaries. Based on computed tomography data of a patients head, our approach aims to extract the border that separates the structures of interest from the rest of the head. This three-dimensional region information is useful in many clinical applications, e.g. diagnosis, surgical simulation, surgical pl...

In this paper a new approach to CT based investigation of pulmonary airways is introduced. Especially a new fully automated algorithm for airway tree segmentation is proposed. The algorithm is based on 3D seeded region growing. However in opposite to traditional approaches region growing is applied twice: firstly – for detecting main bronchi, secondly – for localizing low order parts of the air...

Human ovarian physiology is still poorly understood, with the factors and mechanisms that control initiation of follicular recruitment and loss remaining particularly unclear. Conventional hypothesis-led studies provide new data, results and insights, but datasets from individual studies are often small, allowing only limited interpretation. Great power is afforded by the aggregation of data fr...

This article presents a novel 3D planar patch extraction method using a probabilistic region growing algorithm. Our method works by simultaneously initiating multiple planar patches from seed points, the latter determined by an intensity-based 2D segmentation algorithm in the stereopair images. The patches are grown incrementally and in parallel as 3D scene points are considered for membership,...

In this paper, a target tracking algorithm is proposed by combining the improved Mean Shift algorithm with the adaptive Kalman filter. For a selected moving object, frame difference and region growing methods are used to segment target and extract the dominant color. In the tracking process, the initial iterative position is obtained by adaptive Kalman filter in each frame. The tracking result ...

This report reviews selected image binarization and segmentation methods that have been proposed and which are suitable for the processing of volume images. The focus is on thresholding, region growing, and shape–based methods. Rather than trying to give a complete overview of the field, we review the original ideas and concepts of selected methods, because we believe this information to be imp...

A new technique, named DPOL MUM, is proposed for the segmentation of multi-frequency polarimetric SAR images. By following a Maximum Likelihood approach, an optimal split-merge test is derived, which fully exploits the characteristic block-diagonal structure of the data covariance matrix. This test is demonstrated to yield a reduced fluctuation error than the extension of the polarimetric test ...

We propose a novel approach for the detection of temporally impulsive dirt impairments in archived film sequences. Our method does not require motion compensation and uses raw differences between the current frame and each of the previous and next frames to extract a confidence signal which is used to localize and label dirt regions. A key feature of our method is the removal of false alarms by...

Preprocessing of character images is an important step in recognition. We describe a method of enhancing binary character images to assist the subsequent recognition process in both handwritten and machine-printed documents. The method performs selective and adaptive stroke "filling" with a neighborhood operator which emphasizes stroke connectivity. An improvement of 7% points was realized in r...

This work presents a segmentation method based on a region growing approach. It has been implemented in the geographic information and image processing system (SPRING) which has been developed at INPE. The technique is applied to segment images which are being used to assess land use changes in the Amazon region. Segmented Landsat-TM images are shown to illustrate the technique.