Improved watershed transform for tumor segmentation: Application to mammogram image compression

In this study, an automatic image segmentation method is proposed for the tumor segmentation from mammogram images by means of improved watershed transform using prior information. The segmented results of individual regions are then applied to perform a loss and lossless compression for the storage efficiency according to the importance of region data. These are mainly performed in two procedures, including region segmentation and region compression. In the first procedure, the canny edge detector is used to detect the edge between the background and breast. An improved watershed transform based on intrinsic prior information is then adopted to extract tumor boundary. Finally, the mammograms are segmented into tumor, breast without tumor and background. In the second procedure, vector quantization (VQ) with competitive Hopfield neural network (CHNN) is applied on the three regions with different compression rates according to the importance of region data so as to simultaneously reserve important tumor features and reduce the size of mammograms for storage efficiency. Experimental results show that the proposed method gives promising results in the compression applications.

► An automatic method is proposed for tumor segmentation from mammogram images by improved watershed transform.
► Segmented regions are applied to perform a loss and lossless compression according to the importance of region data.
► An improved watershed transform based on intrinsic prior information is adopted to extract tumor boundary.
► Vector quantization with competitive Hopfield neural network is applied on three regions with different compression rates.