Kalshetti Pratik, Bundele Manas, Rahangdale Parag, Jangra Dinesh, Chattopadhyay Chiranjoy, Harit Gaurav, Elhence Abhay
Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur 342011, Rajasthan, India.
Department of Orthopaedics, All India Institute of Medical Sciences, Jodhpur 342005, Rajasthan, India.
Comput Biol Med. 2017 Apr 1;83:22-33. doi: 10.1016/j.compbiomed.2017.02.002. Epub 2017 Feb 13.
Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images.
在临床评估中，分割通常用于医学图像以识别疾病。因此，它已成为主要研究领域之一。传统的图像分割技术无法为医学图像提供令人满意的分割结果，因为医学图像包含不规则性。在分割之前需要对它们进行预处理。为了获得最适合医学图像分割的方法，我们提出了MIST（医学图像分割工具），这是一种两阶段算法。第一阶段使用数学形态学自动生成感兴趣区域的二值标记图像。该标记用作第二阶段的掩码图像，第二阶段使用GrabCut产生高效的分割结果。所获得的结果可以通过用户交互进一步细化，这可以使用所提出的图形用户界面（GUI）来完成。实验结果表明，该方法准确，并且在医学图像和自然图像上以最少的用户交互提供了令人满意的分割结果。
