No abstract

Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation; however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images

No abstract

No abstract

Ultrasound (US) image segmentation is a difficult task because of its heavy speckle noise, low quality and blurry boundaries. In this paper, a new neural network based method is proposed for ultrasound images segmentation. A modified self organizing map (SOM) network, named finite impulse response SOM (FIR-SOM), is utilized to segment ultrasound images. A two dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. Experimental results show that FIR-SOM discovers the pattern of the input image properly and is robust against noise. Segmentation results of breast ultrasound images (BUS) demonstrate that there is a strong correlation between tumor region selected by a physician and the tumor region segmented by our proposed method.Index Terms-Artificial neural network (ANN), ultrasound image (US) segmentation, computer aided diagnosis (CAD) systems.