Vieira Pedro, Ramos Jaime, Barbosa Daniel, Roupar Dalila, Silva Carlos, Correia Higino, Lima Carlos S
Industrial Electronics Department of University of Minho, Campus de Azurem, 4800-058 Guimaraes Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4010-3. doi: 10.1109/EMBC.2012.6346846.
State of the art algorithms for diagnosis of the small bowel by using capsule endoscopic images usually rely on the processing of the whole frame, hence no segmentation is usually required. However, some specific applications such as three-dimensional reconstruction of the digestive wall, detection of small substructures such as polyps and ulcers or training of young medical staff require robust segmentation. Current state of the art algorithms for robust segmentation are mainly based on Markov Random Fields (MRF) requiring prohibitive computational resources not compatible with applications that generate a great amount of data as is the case of capsule endoscopy. However context information given by MRF is not the only way to improve robustness. Alternatives could come from a more effective use of the color information. This paper proposes a Maximum A Posteriori (MAP) based approach for lesion segmentation based on pixel intensities read simultaneously in the three color channels. Usually tumor regions are characterized by higher intensity than normal regions, where the intensity can be measured as the vectorial sum of the 3 color channels. The exception occurs when the capsule is positioned perpendicularly and too close to the small bowel wall. In this case a hipper intense tissue region appears at the middle of the image, which in case of being normal tissue, will be segmented as tumor tissue. This paper also proposes a Maximum Likelihood (ML) based approach to deal with this situation. Experimental results show that tumor segmentation becomes more effective in the HSV than in the RGB color space where diagonal covariance matrices have similar effectiveness than full covariance matrices.
使用胶囊内镜图像诊断小肠的先进算法通常依赖于对整个帧的处理，因此通常不需要分割。然而，一些特定应用，如消化道壁的三维重建、息肉和溃疡等小亚结构的检测或年轻医务人员的培训，需要鲁棒的分割。目前用于鲁棒分割的先进算法主要基于马尔可夫随机场（MRF），需要大量计算资源，这与产生大量数据的应用（如胶囊内镜检查）不兼容。然而，MRF提供的上下文信息并不是提高鲁棒性的唯一方法。替代方法可能来自更有效地利用颜色信息。本文提出了一种基于最大后验概率（MAP）的方法，用于基于在三个颜色通道中同时读取的像素强度进行病变分割。通常，肿瘤区域的特征是强度高于正常区域，其中强度可以作为三个颜色通道的矢量和来测量。当胶囊垂直放置且离小肠壁太近时会出现例外情况。在这种情况下，图像中间会出现一个高强度组织区域，如果这是正常组织，将被分割为肿瘤组织。本文还提出了一种基于最大似然（ML）的方法来处理这种情况。实验结果表明，在HSV颜色空间中肿瘤分割比在RGB颜色空间中更有效，其中对角协方差矩阵与全协方差矩阵具有相似的有效性。
