Improved ROI Algorithm for Compressing Medical Images

Mohamed Nagy Saad, Ahmed Hisham Kandil

Abstract

The digital medical images have become an essential part of the electronic patient record. These images may be used for screening, diagnosis, treatment and educational purposes. These images have to be stored, archived, retrieved, and transmitted. Compression techniques are extremely useful when considering large quantities of these images. In this paper, four compression techniques are applied on three medical image modalities. The compression techniques are either lossless or lossy techniques. The applied lossless techniques are Huffman and Arithmetic. The applied lossy techniques are Discrete Cosine Transform (DCT) and Wavelet. The modalities are Ultrasound (US), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The observed parameters are both the compression ratio (CR) and total compression time (TCT) (compression time + decompression time). The target is to maximize the CR while preserving images’ information using the best compression technique. The maximum accepted CR for each image is chosen by three experts. The last enhancement is done by isolating the region of interest (ROI) in the image then applying the compression procedure. Applying the ROI technique on the studied cases by the experts gave promising results.

References

  1. Acharya, T. and Tsai, P., 2005. JPEG2000 Standard for Image Compression Concepts, Algorithms and VLSI Architectures. New Jersey: Wiley.
  2. Clunie, D., 2000. Lossless Compression of Grayscale Medical Images - Effectiveness of Traditional and State of the Art Approaches. Proceedings of SPIE, Medical Imaging, 3980, 74-84.
  3. Kesavamurthy, T., Rani, S., Malmurugan, N., 2009. Volumetric Color Medical Image Compression for PACS Implementation. IJBSCHS, 14(2), 3-10.
  4. Nait-Ali, A. and Cavaro-Menard, C., 2008. Compression of Biomedical Images and Signals. London, New Jersey: ISTE and Wiley.
  5. Terae, S., Miyasaka, K., Kudoh, K., Nambu, T., Shimizu, T., Kaneko, K., Yoshikawa, H., Kishimoto, R., Omatsu, T., Fujita, N., 2000. Wavelet Compression on Detection of Brain Lesions with Magnetic Resonance Imaging. Journal of Digital Imaging, 13(4), 178-190.
  6. Tolba, S., 2002. Wavelet Packet Compression of Medical Images. Elsevier Science, Digital Signal Processing, 12, 441-470.
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Paper Citation


in Harvard Style

Nagy Saad M. and Hisham Kandil A. (2013). Improved ROI Algorithm for Compressing Medical Images . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013) ISBN 978-989-8565-34-1, pages 184-189. DOI: 10.5220/0004325301840189


in Bibtex Style

@conference{biodevices13,
author={Mohamed Nagy Saad and Ahmed Hisham Kandil},
title={Improved ROI Algorithm for Compressing Medical Images},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013)},
year={2013},
pages={184-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004325301840189},
isbn={978-989-8565-34-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013)
TI - Improved ROI Algorithm for Compressing Medical Images
SN - 978-989-8565-34-1
AU - Nagy Saad M.
AU - Hisham Kandil A.
PY - 2013
SP - 184
EP - 189
DO - 10.5220/0004325301840189