Segmentation of acoustic images by neural network processing

A segmentation method for biomedical acoustic images is reported which efficiently classifies the groups of similar image elements (pixels) and separates them into particular characteristic regions. As the input data, the method uses the pixel intensities of the source image. The classification is performed by learning vector quantization neural networks, which separate the main classes (structures, tissues, artifacts, etc.) present in the image. Because this type of neural network implies that the number of the classes is known and that the network should be trained by instruction, an expert must participate in the process of generating the input data. Results obtained by processing test acoustic (ultrasonic) images demonstrate that the method is capable of effectively solving sonography classification problems. The accuracy of the method is estimated by comparison with the segmentation performed manually.