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A connected component labelling algorithm for greyscale mammography image processing as a pre-processing tool

Yapa R. D., Harada K.

Machine Graphics and Vision

2007

Vol. 16, No. 3/4

305-327

A new algorithm for connected component-labelling is presented in this paper. The proposed algorithm requires only one scan through an image for labelling connected components. Once this algorithm encounters a starting pixel of a component, it traces in full all the contour pixels and all internal pixels of that particular component. The algorithm recognizes components of the image one at a time while scanning in the raster order. This property will be useful in areas such as image matching, image registration, content-based information retrieval and image segmentation. It is also capable of extracting the contour pixels of an image and storing them in a clock-wise directional order, which will provide useful information in many applications. The algorithm assigns consecutive label numbers to different components, and therefore requires a minimum number of labels. We have used the algorithm in mammography image processing as a pre-processing tool, and have demonstrated the possibility of using it for breast tissue segmentation and for detecting regions of interest in breast tissue. Another important advantage of the algorithm is that it can be used as a content-based image retrieval tool for retrieving images based on the visual contents of a given image. This would be very useful in retrieving related images from large scale medical databases.

Using connected components to quide image understanding and segmentation

Wang Y., Bhattacharya P.

Machine Graphics and Vision

2003

Vol. 12, No. 2

163-186

In this paper, we propose a method for understanding an image with the help of the theory of parameter-dependent connected components developed by us in a previous work. We may study various properties of an image at the connected component level, from the low level vision to an intermediate level vision. Using the information obtained from various component histograms and certain pre-knowledge, we describe how to select suitable values of the parameters so that an object in a gray image may be represented by a parameter-dependent component. Segmentation of the object could be conducted by locating the corresponding component. Our approach can be applied to a wide variety of images as we do not make any assumptions about the image formation model, and the method is independent of the type of the grid system used for the digitization process and the type of pixel adjacency relation.