A machine learning approach for interactive lesion segmentation

Yuanzhong Li; Shoji Hara; Wataru Ito; Kazuo Shimura

doi:10.1117/12.708910

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5 March 2007 A machine learning approach for interactive lesion segmentation

Yuanzhong Li, Shoji Hara, Wataru Ito, Kazuo Shimura

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Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 651246 (2007) https://doi.org/10.1117/12.708910
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

In this paper, we propose a novel machine learning approach for interactive lesion segmentation on CT and MRI images. Our approach consists of training process and segmenting process. In training process, we train AdaBoosted histogram classifiers to classify true boundary positions and false ones on the 1-D intensity profiles of lesion regions. In segmenting process, given a marker indicating a rough location of a lesion, the proposed solution segments its region automatically by using the trained AdaBoosted histogram classifiers. If there are imperfects in the segmented result, based on one correct location designated by the user, the solution does the segmentation again and gives a new satisfied result. There are two novelties in our approach. The first is that we use AdaBoost in the training process to learn diverse intensity distributions of lesion regions, and utilize the trained classifiers successfully in segmenting process. The second is that we present a reliable and user-friendly way in segmenting process to rectify the segmented result interactively. Dynamic programming is used to find a new optimal path. Experimental results show our approach can segment lesion regions successfully, despite the diverse intensity distributions of the lesion regions, marker location variability and lesion region shape variability. Our framework is also generic and can be applied for blob-like target segmentation with diverse intensity distributions in other applications.

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Yuanzhong Li, Shoji Hara, Wataru Ito, and Kazuo Shimura "A machine learning approach for interactive lesion segmentation", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651246 (5 March 2007); https://doi.org/10.1117/12.708910

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