A unified framework for image compression and segmentation by using an incremental neural network

This paper presents a novel unified framework for compression and decision making by using artificial neural networks. The proposed framework is applied to medical images like magnetic resonance (MR), computer tomography (CT) head images and ultrasound image. Two artificial neural networks, Kohonen map and incremental self-organizing map (ISOM), are comparatively examined. Compression and decision making processes are simultaneously realized by using artificial neural networks. In the proposed method, the image is first decomposed into blocks of 8 × 8 pixels. Two-dimensional discrete cosine transform (2D-DCT) coefficients are computed for each block. The dimension of the DCT coefficients vectors (codewords) is reduced by low-pass filtering. This way of dimension reduction is known as vector quantization in the compression scheme. Codewords are the feature vectors for the decision making process. It is observed that the proposed method gives higher compression rates with high signal to noise ratio compared to the JPEG standard, and also provides support in decision-making by performing segmentation.