Rough Segmentation Result Research Articles

皮肤老化分析中的皮沟检测方法研究<br>Research on Detecting Skin Grooves in Skin Aging Analysis

皮沟检测是客观量化人体皮肤老化程度的一个关键问题，检测精度直接影响到后续皮肤表面二维几何特征的计算。本文提出了一种基于数据驱动和模型驱动的混合控制策略来检测皮沟。其中数据驱动的控制先对输入图像进行一系列增强处理，再进行分水岭变换以获得粗略的检测结果；而模型驱动的控制则利用数据驱动处理的结果及视觉感知的先验知识建立一个区域合并模型，该模型被用于移除粗略检测结果中的虚假皮沟。主观和客观实验表明，对不同粒度的皮肤图像，提出的方法均能精确地检测出皮沟，并能有效地抑制虚假皮沟的出现。 Detecting skin grooves plays an important role in the objective quantification of human skin aging, and the detection accuracy directly influences the subsequent computation of two-dimensional geometrical characteristics of the skin surface. Based on the combined control strategies consisting of data-driven and model-driven control, a novel seg-mentation approach to skin grooves detection was proposed in this paper. In data-driven control, the rough segmentation result was obtained by applying watershed transform to the enhanced image. In model-driven control, the information acquired from the data-driven control and prior knowledge derived from sense of vision were used for constructing a region merging model. The model was used for removing redundant watershed lines. Subjective and objective evalua-tions demonstrate the favorable performance of the proposed method in detecting precisely skin grooves and restraining false skin grooves.

Image matting for fusion of multi-focus images in dynamic scenes

In this paper, we address the problem of fusing multi-focus images in dynamic scenes. The proposed approach consists of three main steps: first, the focus information of each source image obtained by morphological filtering is used to get the rough segmentation result which is one of the inputs of image matting. Then, image matting technique is applied to obtain the accurate focused region of each source image. Finally, the focused regions are combined together to construct the fused image. Through image matting, the proposed fusion algorithm combines the focus information and the correlations between nearby pixels together, and therefore tends to obtain more accurate fusion result. Experimental results demonstrate the superiority of the proposed method over traditional multi-focus image fusion methods, especially for those images in dynamic scenes.

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

We propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation. The algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results. The effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%. This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.

Liver Tumor Segmentation Using Implicit Surface Evolution

A method for automatic liver tumor segmentation from computer tomography (CT) images is presented in this paper. Segmentation is an important operation before surgery planning, and automatic methods offer an alternative to laborious manual segmentation. In addition, segmentations of automatic methods are reproducible, so they can be reliably evaluated and they do not depend on the performer of the segmentation. In this work, the segmentation is performed in two stages. First a rough segmentation of tumors is obtained by simple thresholding and morphological operations. The second stage refines the rough segmentation result using fuzzy clustering and a geometric deformable model (GDM) that is fitted on the clustering result. The method was evaluated with data provided by Liver Tumor Segmentation Challenge 08, to which the method also participated. The data included 10 images from which 20 tumors were segmented. The method showed promising results.

Template-driven segmentation of confocal microscopy images

High quality 3D visualization of anatomic structures is necessary for many applications. The anatomic structures first need to be segmented. A variety of segmentation algorithms have been developed for this purpose. For confocal microscopy images, the noise introduced during the specimen preparation process, such as the procedure of penetration or staining, may cause images to be of low contrast in some regions. This property will make segmentation difficult. Also, the segmented structures may have rugged surfaces in 3D visualization. In this paper, we present a hybrid method that is suitable for segmentation of confocal microscopy images. A rough segmentation result is obtained from the atlas-based segmentation via affine registration. The boundaries of the segmentation result are close to the object boundaries, and are regarded as the initial contours of the active contour models. After convergence of the snake algorithm, the resulting contours in regions of low contrast are locally refined by parametric bicubic surfaces to alleviate the problem of incorrect convergence. The proposed method increases the accuracy of the snake algorithm because of better initial contours. Besides, it can provide smoother segmented results in 3D visualization.