IEEE Transactions on Pattern Analysis and Machine Intelligence
The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Features and classification methods to locate deciduous trees in images
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
IEEE Transactions on Computers
Optimal pyramidal and subband decompositions for hierarchical coding of noisy and quantized images
IEEE Transactions on Image Processing
Use of nonlinear principal component analysis and vector quantization for image coding
IEEE Transactions on Image Processing
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
An adaptive counter propagation network based on soft competition
Pattern Recognition Letters
Review of brain MRI image segmentation methods
Artificial Intelligence Review
Using intelligence techniques to predict postoperative morbidity of endovascular aneurysm repair
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Improved watershed transform for tumor segmentation: Application to mammogram image compression
Expert Systems with Applications: An International Journal
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Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
| Hi-index | 12.05 |
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 8x8 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.