Medical Image and Signal Processing Research center, Isfahan University of Medical Sciences, Isfahan, 81745-319, Iran.
School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Med Biol Eng Comput. 2019 Feb;57(2):379-388. doi: 10.1007/s11517-018-1876-2. Epub 2018 Aug 20.
Early detection of breast tumors, feet pre-ulcers diagnosing in diabetic patients, and identifying the location of pain in patients are essential to physicians. Hot or cold regions in medical thermographic images have potential to be suspicious. Hence extracting the hottest or coldest regions in the body thermographic images is an important task. Lazy snapping is an interactive image cutout algorithm that can be applied to extract the hottest or coldest regions in the body thermographic images quickly with easy detailed adjustment. The most important advantage of this technique is that it can provide the results for physicians in real time readily. In other words, it is a good interactive image segmentation algorithm since it has two basic characteristics: (1) the algorithm produces intuitive segmentation that reflects the user intent with given a certain user input and (2) the algorithm is efficient enough to provide instant visual feedback. Comparing to other methods used by the authors for segmentation of breast thermograms such as K-means, fuzzy c-means, level set, and mean shift algorithms, lazy snapping was more user-friendly and could provide instant visual feedback. In this study, twelve test cases were presented and by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for these twelve cases. It was concluded that lazy snapping was much faster than other methods applied by the authors such as K-means, fuzzy c-means, level set, and mean shift algorithms for segmentation. Graphical abstract Time taken to implement lazy snapping algorithm to extract suspicious regions in different presented thermograms (in seconds). In this study, ten test cases are presented that by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for the ten cases. It concludes lazy snapping is much faster than other methods applied by the authors.
早期发现乳房肿瘤、诊断糖尿病患者足部溃疡前病变、确定患者疼痛部位，这些对医生来说至关重要。医学热成像图像中的热区或冷区可能存在可疑之处。因此，提取人体热图像中的最热或最冷区域是一项重要任务。Lazy snapping 是一种交互式图像裁剪算法，可用于快速轻松地进行详细调整，提取人体热图像中的最热或最冷区域。该技术最重要的优势在于它可以为医生提供实时结果。换句话说，它是一种很好的交互式图像分割算法，因为它具有两个基本特征：（1）算法生成直观的分割，反映了给定一定用户输入的用户意图；（2）算法效率足够高，可以提供即时的视觉反馈。与作者用于分割乳房热图的其他方法（如 K-means、模糊 c-均值、水平集和均值漂移算法）相比，Lazy snapping 更加用户友好，可以提供即时的视觉反馈。在这项研究中，呈现了十二个测试案例，并通过应用 Lazy snapping 算法，从相应的人体热图像中提取了最热或最冷的区域。这十二个案例看到结果的时间从 7 秒到 30 秒不等。结论是，与作者应用的其他方法（如 K-means、模糊 c-均值、水平集和均值漂移算法）相比，Lazy snapping 更快。
