Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction

Abstract

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment

“…The performance of the proposed near-lossless compression is compared with several state-of-the-art algorithms such as JPEG-LS, JPEG 2000 [36], CALIC [37] and SPIHT [38], and spatial prediction [2] for 8-bit dataset. Table 8 shows comparison of bit rate and PSNR results averaged across dataset obtained by our proposed method for the same dataset.…”

“…Volumetric 8-bit CT and MRI database are used for evaluation of the proposed technique. The experimental results indicate that the medical images are efficiently compressed by the proposed algorithm and provide better quality in terms PSNR than the other near-lossless approaches [2]. Boopathiraja and Kalavathi proposed a near-lossless coding technique for multispectral images and the wavelet transformation is employed with Huffman encoder to encode the image efficiently.…”

“…To deal with these issues, compression of medical image data is required. Lossless compression techniques are traditionally preferred for medical images to avoid false diagnosis, although the compression ratio (CR) achieved is quite low, ranging from 2 to 4 depending upon the employed methods [2]. Lossy compression is an irreversible technique for image compression, which provides good compression efficiency but degrades the image quality.…”

“…Comparison of BPP and PSNR for proposed technique and some other techniques Image Database CALIC [37] JPEG-LS [36] JPEG-2000[36] SPIHT[38] Spatial Prediction[2] …”

Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding

“…Tests were performed on benchmark images and the results give similar or better results than standard algorithms like JPG2000 and median compression [18]. Song et al proposed adaptive block size based spatial prediction to predict blocks and before quantization, a residual image is transformed by the lossless Hadamard transform to improve the recovered image quality [19]. Song et al proposed an efficient algorithm based on irregular segmentation and region-based prediction to improve compression rates for lossless compression of medical images.…”