Compression in Medical Images (Paper Survey)

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Compression in Medical Images(Paper Survey)

• Mohammed Jirari
• Fall 2002
• CS 74401

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Need for Image Compression in Medical Images

• Image compression plays an important role in
  telematics applications and especially in
  telemedicine.
• Diagnosis is effective only when compression
  techniques preserve all the relevant info needed.
  (Lossless)

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Lossy Compression

• More efficient in storage and transmission.
• No guarantee that characteristics needed for
  medical image diagnosis are preserved.
• Image characteristics are preserved in the
  coefficients of the domain space in which the
  original image is transformed. (In DWT, the
  wavelet coefficients keep all the info).
• Goal is to discard only the irrelevant wavelet
  coefficients according to a criterion (magnitude
  of values).

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2 Proposed Methods

• Applying different thresholds to uniquely defined
  regions of the transform domain (opposed to the
  DWT where the same threshold is applied to the
  whole image).
• Separately, applying the same transformation to
  the regions of interest in which the image could
  be divided according to a predetermined
  characteristic (texture).

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First Method

• The original image is transformed via the 2D DWT
  into bands of wavelet coefficients.
• Fuzzy c-means clustering is applied to each band,
  dividing it into 2 classes.
• Important regions have lower compression ratio
  than non-important ones.
• To reconstruct, the inverse 2D DWT is applied to
  the remaining wavelet coefficients (ones removed
  during compression are equal to 0).

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Second Method

• Cluster the image into 2 classes (significant and
  non-significant textural regions).
• Texture identification analysis is performed
  based on 4 cooccurrence matrices
• Energy Angular Second Moment
• Correlation

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Second Method (cont.)

• Inverse Difference Moment
• Entropy

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Second Method (cont.)

• When pattern is texturally significant and
  cooccurrence matrices are used, then upper left
  point of the corresponding sliding window takes
  on label 255, otherwise 0. (for each gray level
  image a new black-white image IMP results)
• Decompose the original image into 2 images G1 and
  G2 by
• G1(x0,y0)MIN(G0(x0,y0),IMPG0(x0,y0))
  G2(x0,y0)MIN(G0(x0,y0),255-IMPG0(x0,y0))

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Second Method (cont.)

• 2D DWT is applied to G1 and G2 (compression ratio
  for DWT-G1 is 60 and DWT-G2 is 80, we get
  DWT-G1 and DWT-G2 respectively).
• Reconstruct the image by
• G0aINV-2D-DWT-(DWT-G1)
• bINV-2D-DWT-(DWT-G2)
• NOTE Main problem is the elimination of
  blocking effects in the partitions boundaries
  (need to smooth the reconstructed image in these
  boundaries).

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Region Of Interest Colon CT Image Compression
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Segmentation of ROI

• Segmenting the colon from the CT data sets
  consists of three steps
• The air is separated away from the tissue
  by intensity thresholding
• The colon wall that surrounds the air is
  extracted by 3D extension of Sobels derivative
  operation.
• A morphological 3D grassfire operation
  determines the colon-wall region within 5- pixel
  margin.

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ROI Based Compression Scheme

• Once the ROI is segmented in each slice
• A hybrid compression scheme is used for coding
  the images.
• The first slice of the volume is compressed with
  a lossless coder.
• Each slice is then coded by motion compensated
  coding.
• The difference between the real image ROI block
  and the predicted ROI block is coded by an
  entropy minimizing lossless coder(Huffman).

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