Preliminary validation of content-Preliminary validation of content-based compression of mammographic based compression of mammographic

imagesimages

Brad GrinsteadHamed Sari-Sarraf, Shaun Gleason,

and Sunanda Mitra

Funded in part by: National Science Foundation

AbstractAbstract

This paper presents some preliminary validation results from the content-basedcompression (CBC) of digitized mammograms for transmission, archiving, and,ultimately, telemammography. Unlike traditional compression techniques,CBC is a process by which the content of the data is analyzed before thecompression takes place. In this approach the data is partitioned into twoclasses of regions and a different compression technique is performed on eachclass. The intended result achieves a balance between data compression anddata fidelity. For mammographic images, the data is segmented into two non-overlapping regions: (1) background regions, and (2) focus-of-attentionregions (FARs) that contain the clinically important information.Subsequently, the former regions are compressed using a lossy technique,which attains large reductions in data, while the latter regions are compressedusing a lossless technique in order to maintain the fidelity of these regions. Inthis case, results show that compression ratios averaging 5-10 times greaterthan that of lossless compression alone can be achieved, while preserving thefidelity of the clinically important information.

OverviewOverview• Objective

– To Make Telemammography More Viable– Decrease Transmission Time – Decrease Storage Requirements

• Concept– Fractal-Based Automatic Data Segmentation

– Divides the Mammogram into 2 regions

• Background Regions• Focus-of-Attention Regions (FARs)

– Combination of Lossy and Lossless Encoding– Decreases Storage Requirements While Preserving Detail

MotivationMotivation

• When Talking About Compression of Medical Images, There Are Two Camps

– Lossless Compression– Preserves Detail

– Lossy Compression– Reduces Storage Requirements

• Content-Based Compression (CBC) Allows Us to Please Both Camps By Offering More Compression, While Preserving Detail in the Areas of Interest

Content-Based Compression ApproachContent-Based Compression Approach

Lossy Compression80:1

Lossless Compression2:1

FAR17% of Image

Background83% of Image

Total Compression15:1While

Preserving Vital

Information

Fractal AnalysisFractal Analysis

Digitized Mammogram or

Synthesized Fractal

Input Image

Quadtree Partition

FARs

Selected Subset

Microcalcifications Have Been Circled for Ease of Viewing

Combination of Compression TechniquesCombination of Compression Techniques

Original Image

80:1 Lossy Coding of

Entire Image

Superposition of Losslessly Encoded FARs Over Lossy

ImageCR=11.52

FARs That Will Be Losslessly Encoded

CBC Software Flow for a Single Sub-ImageCBC Software Flow for a Single Sub-Image

START

Combine Compression Results

Perform Lossless Compression

Perform FAR Generation on Sub-Image

Area Opening

END

Read in Sub-image

Perform Lossy Compression

Encode FAR Locations and Data

CBC ResultsCBC Results

Threshold

Average Percent of

Image Contained w/in FARs

Average Percent of

Micro- calcifications

Contained w/in FARs

Average Compression

Min Compression

Max Compression

Median Compression

2.0 15.10 82.48% 8.42 2.78 16.84 8.171.9 17.52 88.89% 7.41 2.39 14.69 6.751.8 20.29 93.02% 6.37 2.23 12.50 5.901.7 23.45 95.16% 5.52 2.12 9.96 5.26

Lossless 2.05 1.38 3.28 2.00

Threshold

Average Percent of

Image Contained w/in FARs

Average Percent of

Micro- calcifications

Contained w/in FARs

Average Compression

Min Compression

Max Compression

Median Compression

1.50 11.38 83.13% 18.04 8.55 45.66 14.741.45 13.63 87.86% 15.24 7.44 37.84 12.261.40 16.26 89.09% 12.83 6.23 32.64 10.181.35 19.27 90.95% 10.70 5.35 28.01 8.621.30 22.59 92.18% 9.08 4.70 24.19 7.351.25 26.19 93.00% 7.70 4.17 20.96 6.32

Lossless 1.60 1.42 2.73 1.69

100-micron Data

50-micron Data

CAD System Used for ValidationCAD System Used for Validation

Region Growing

LabelingFeature Extraction

Local Thresholding

Global ThresholdingBreast Segmentation Convolution

Module 1

Module 2

Module 3

Digitized Mammogram

Screening Result

The Output of Module 1 is Used for Validation Purposes

Application of CAD Module 1 to Original Application of CAD Module 1 to Original Sub-imageSub-image

Microcalcifications Have Been Circled for Ease of Viewing

Sub-image

Result of Convolution

Thresholding Result

Application of CAD Module 1 to CBC Sub-Application of CAD Module 1 to CBC Sub-image (CR=6.4:1)image (CR=6.4:1)

Microcalcifications Have Been Circled for Ease of Viewing

Sub-image

Result of Convolution

Thresholding Result

Validation ResultsValidation Results

• For the Highest Compression Ratio and Lowest Microcalcification Coverage Rate, 93% of the Microcalcifications Were Detected

• For the Lowest Compression Ratio and Highest Microcalcification Coverage Rate, 97%of the Microcalcifications Were Detected

– This shows that the 80:1 compression ratio leaves some of the information outside of FARs intact, while achieving decent compression

– Higher compression ratios will introduce too much distortion, causing microcalcifications outside of FARs to be completely missed

– In addition, context information contained in the background tissue, which is useful to radiologists, has been preserved

Validation ResultsValidation Results

• The Mammogram That Had the Highest Compression Ratio Also Had the Highest Detection Rate

– This Suggests That There is Not a Direct Relationship Between Microcalcification Detection and the Compression Ratio

Threshold

Average Percent of Image

Contained w/in FARs

Average Percent of Microcalcifications

Contained w/in FARs

Average Percent of Micro-

calcifications Detected by CAD

Module 1

Average Compression

2.0 15.10 82.48% 93.02% 8.421.9 17.52 88.89% 94.44% 7.411.8 20.29 93.02% 96.15% 6.371.7 23.45 95.16% 96.87% 5.52

Lossless 2.05

100-micron Data

Concluding RemarksConcluding Remarks

• Summary– To Improve the Viability of Telemammography by

Exploring the Following Concepts:– Focus of Attention Regions

• Use the Partial Self-Similarity Inherent in Images to Reduce the Input Data

• Use Quadtree Fractal Encoding to Generate FARs– Content-Based Compression

• Obtain Compression Ratio 5-10 Times Greater Than Lossless Compression Alone, While Preserving the Important Information

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