Yu-Len Huang

Breast cancer diagnosis using self-organizing map for sonography

The purpose of this study was to evaluate the performance of neural network model self-organizing maps (SOM) in the classification of benign and malignant sonographic breast lesions. A total of 243 breast tumors (82 malignant and 161 benign) were retrospectively evaluated. When a sonogram was performed, the analog video signal was captured to obtain a digitized sonographic image. The physician selected the region of interest in the sonography. An SOM model using 24 autocorrelation texture features classified the tumor as benign or malignant. In the experiment, cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance using receiver operating characteristic (ROC) curves. The ROC area index for the proposed SOM system is 0.9357 +/- 0.0152, the accuracy is 85. 6%, the sensitivity is 97.6%, the specificity is 79.5%, the positive predictive value is 70.8%, and the negative predictive value is 98. 5%. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies.

Texture features for DCT-coded image retrieval and classification

The multiresolution wavelet transform has been shown to be an effective technique and achieved very good performance for texture analysis. However, a large number of images are compressed by methods based on the discrete cosine transform (DCT). Hence, the image decompression of the inverse DCT is needed to obtain the texture features based on the wavelet transform for the DCT-coded image. This paper proposes the use of the multiresolution reordered features for texture analysis. The proposed features are directly generated by using the DCT coefficients from the DCT-coded image. Comparisons with the subband-energy features extracted from the wavelet transform, conventional DCT using the Brodatz (1966) texture database indicate that the proposed method provides the best texture pattern retrieval accuracy and obtains a much better correct classification rate. The proposed DCT based features are expected to be very useful and efficient for texture pattern retrieval and classification in large DCT-coded image databases.

MLP interpolation for digital image processing using wavelet transform

We present nonlinear interpolation schemes for image resolution enhancement. The multilayer perceptron (MLP) interpolation schemes based on the wavelet transform and subband filtering are proposed. Because estimating each subimage signal is more effective than estimating the whole image signal, pixels in the low-resolution image are used as the input signal of the MLP to estimate all of the wavelet subimage of the corresponding high-resolution image. The image of increased resolution is finally produced by the synthesis procedure of the wavelet transform. As compared with other popular methods, the results show that the improvement is remarkable.

Error Concealment Using Adaptive Multilayer Perceptrons (MLPs) for Block-Based Image Coding*

Image coding algorithms such as Vector Quantisation (VQ), JPEG and MPEG have been widely used for encoding image and video. These compression systems utilise block-based coding techniques to achieve a higher compression ratio. However, a cell loss or a random bit error during network transmission will permeate into the whole block, and then generate several damaged blocks. Therefore, an efficient Error Concealment (EC) scheme is essential for diminishing the impact of damaged blocks in a compressed image. In this paper, a novel adaptive EC algorithm is proposed to conceal the error for block-based image coding systems by using neural network techniques in the spatial domain. In the proposed algorithm, only the intra-frame information is used for reconstructing the image with damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the PSNR evaluation of a reconstructed image are significantly improved using the proposed EC algorithm.

A New Side-Match Finite-State Vector Quantization Using Neural Networks for Image Coding☆

Abstract The side-match finite-state vector quantization (SMVQ) schemes improve performance over the vector quantization by exploiting the neighboring vector correlations within the image. In this paper, we propose a neural network side-match finite-state vector quantization (NN-SMVQ) scheme that combines the techniques of neural network prediction and the SMVQ coding method. In our coding scheme, the multilayer perceptron network is used to improve the accuracy of side-match prediction by utilizing the property of the neural network nonlinear prediction. The NN-SMVQ scheme not only has the advantages of the SMVQ scheme but also improves the coded image quality. Experimental results are given and comparisons are made using our NN-SMVQ coding scheme and some other coding techniques. In the experiments, our NN-SMVQ coding scheme achieves the better visual quality about edge region and the best PSNR performance at nearly the same bit rate. This new NN-SMVQ scheme is also simple and efficient for the hardware design. Moreover, the new scheme does not adversely affect other useful functions provided by the conventional SMVQ scheme.

Finite-state vector quantization by exploiting interband and intraband correlations for subband image coding

Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. We split the image spectrum into seven nonuniform subbands. Threshold vector quantization (TVQ) and finite state vector quantization (FSVQ) methods are employed in coding the subband images by exploiting interband and intraband correlations. Our new SBC-FSVQ schemes have the advantages of the subband-VQ scheme while reducing the bit rate and improving the image quality. Experimental results are given and comparisons are made using our new scheme and some other coding techniques. In the experiments, it is found that SBC-FSVQ schemes achieve the best peak signal-to-noise ratio (PSNR) performance when compared to other methods at the same bit rate.

Subband finite-state vector quantization

Subband coding and vector quantization have been shown to be effective methods for coding images at low bit rates. In this paper, we propose a new subband finite-state vector quantization scheme that combines the SBC and FSVQ. A frequency band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 subbands. In general, the 16 subbands can be encoded by intra-band VQ or inter-band VQ. We will use the inter-band VQ to exploit the correlations among the subband images. Moreover, the FSVQ is used to improve the performance by using the correlations of the neighboring samples in the same subband. It is well known that the inter- band VQ scheme has several advantages over coding each subband separately. Our subband- FSVQ scheme not only has all the advantages of the inter-band VQ scheme but also reduces the bit rate and improves the image quality. Comparisons are made between our scheme and some other coding techniques. The new scheme yields a good peak signal-to-noise ratio performance in the region between 0.30 and 0.31 bit per pixel, both for images inside and outside a training set of five 512 X 512 mono-chrome images. In the experiments, the improvement of our scheme over the ordinary VQ without SBC is up to 3.42 dB and over the inter-band VQ is up to 1.20 dB at nearly the same bit rate for the image Lena. The PSNR of the encoded image Lena using the proposed scheme is 32.1 dB at 0.31 bit per pixel.

Subband Finite-State Vector Quantization for Still Image Coding

Abstract Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. The basic idea of subband coding is to split up the frequency band of the signal and then to encode the subbands. Reconstruction is performed by decoding and merging the interpolated subband images. In VQ, the image to be encoded is first processed to yield a set of vectors. The input vectors are individually quantized to the closest codewords in the codebook. In this paper, we propose a new subband finite-state vector quantization (SBC-FSVQ) scheme that combines the SBC and the FSVQ. The frequency band decomposition of an image is carried out by means of 2D separable quadrature mirror filters (QMFs). In our coding scheme, we split the image spectrum into sixteen equally sized subbands. The FSVQ is used to improve the performance by using the correlations of the neighboring samples in the same subband. Thus, our SBC-FSVQ scheme not only has the advantages of the SBC-VQ scheme but also reduces the bit rate and improves the image quality. Experimental results are given and comparisons are made using our new schemes and some other coding techniques. Our technique yields good PSNR performance, for images both inside and outside a training set of five 512 × 512 images. In the experiments, it is found that our SBC-FSVQ scheme achieves the best PSNR performance at nearly the same bit rate.