- Conference Article
11
- 10.1117/12.839920
Ball-scale based hierarchical multi-object recognition in 3D medical images
- Mar 04, 2010
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
- Ulas Bağci + 2 more +2
This paper investigates, using prior shape models and the concept of ball\nscale (b-scale), ways of automatically recognizing objects in 3D images without\nperforming elaborate searches or optimization. That is, the goal is to place\nthe model in a single shot close to the right pose (position, orientation, and\nscale) in a given image so that the model boundaries fall in the close vicinity\nof object boundaries in the image. This is achieved via the following set of\nkey ideas: (a) A semi-automatic way of constructing a multi-object shape model\nassembly. (b) A novel strategy of encoding, via b-scale, the pose relationship\nbetween objects in the training images and their intensity patterns captured in\nb-scale images. (c) A hierarchical mechanism of positioning the model, in a\none-shot way, in a given image from a knowledge of the learnt pose relationship\nand the b-scale image of the given image to be segmented. The evaluation\nresults on a set of 20 routine clinical abdominal female and male CT data sets\nindicate the following: (1) Incorporating a large number of objects improves\nthe recognition accuracy dramatically. (2) The recognition algorithm can be\nthought as a hierarchical framework such that quick replacement of the model\nassembly is defined as coarse recognition and delineation itself is known as\nfinest recognition. (3) Scale yields useful information about the relationship\nbetween the model assembly and any given image such that the recognition\nresults in a placement of the model close to the actual pose without doing any\nelaborate searches or optimization. (4) Effective object recognition can make\ndelineation most accurate.\n
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